source_paper
string | source_section
string | premise
string | target_doi
string | target_paper
string |
|---|---|---|---|---|
PMC10007007
|
Discussion
|
Third, in 20% (3/15) of studies, the humanistic yet nonhumanistic construct of AI chatbots provided a safe space for the users to discuss, share, and ask for information on sensitive issues [5,22,23,35].
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Discussion
|
Third, in 20% (3/15) of studies, the humanistic yet nonhumanistic construct of AI chatbots provided a safe space for the users to discuss, share, and ask for information on sensitive issues [5,22,23,35].
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Discussion
|
Third, in 20% (3/15) of studies, the humanistic yet nonhumanistic construct of AI chatbots provided a safe space for the users to discuss, share, and ask for information on sensitive issues [5,22,23,35].
|
10.2196/17530
|
PMC7215523
|
PMC10007007
|
Discussion
|
This finding is consistent with a previous systematic review that reported the use of anonymity for encouraging users to freely express their emotions [13].
|
10.2196/jmir.7351
|
PMC5709656
|
PMC10007007
|
Discussion
|
Therefore, public health professionals and health care providers can consider the integration of AI chatbots into existing services as a support tool, rather than a replacement [9].
|
10.2196/16021
|
PMC7385637
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic reviews that reported the integration of AI chatbots into diverse platforms, such as Slack (Slack Technologies, LLC), Messenger, WhatsApp, and Telegram [1,2,14], and the use of a large sample size, such as >100 participants in 10 of 15 studies [21,23,24,26,27,29-33].
|
10.1093/jamia/ocy072
|
PMC6118869
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic reviews that reported the integration of AI chatbots into diverse platforms, such as Slack (Slack Technologies, LLC), Messenger, WhatsApp, and Telegram [1,2,14], and the use of a large sample size, such as >100 participants in 10 of 15 studies [21,23,24,26,27,29-33].
|
10.1186/s12966-021-01224-6
|
PMC8665320
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic reviews that reported the integration of AI chatbots into diverse platforms, such as Slack (Slack Technologies, LLC), Messenger, WhatsApp, and Telegram [1,2,14], and the use of a large sample size, such as >100 participants in 10 of 15 studies [21,23,24,26,27,29-33].
|
10.2196/20701
|
PMC7522733
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic reviews that reported the integration of AI chatbots into diverse platforms, such as Slack (Slack Technologies, LLC), Messenger, WhatsApp, and Telegram [1,2,14], and the use of a large sample size, such as >100 participants in 10 of 15 studies [21,23,24,26,27,29-33].
|
10.2196/15085
|
PMC7267999
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic reviews that reported the integration of AI chatbots into diverse platforms, such as Slack (Slack Technologies, LLC), Messenger, WhatsApp, and Telegram [1,2,14], and the use of a large sample size, such as >100 participants in 10 of 15 studies [21,23,24,26,27,29-33].
|
10.2196/17530
|
PMC7215523
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic reviews that reported the integration of AI chatbots into diverse platforms, such as Slack (Slack Technologies, LLC), Messenger, WhatsApp, and Telegram [1,2,14], and the use of a large sample size, such as >100 participants in 10 of 15 studies [21,23,24,26,27,29-33].
|
10.1177/2055207619880676
|
PMC6775545
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic reviews that reported the integration of AI chatbots into diverse platforms, such as Slack (Slack Technologies, LLC), Messenger, WhatsApp, and Telegram [1,2,14], and the use of a large sample size, such as >100 participants in 10 of 15 studies [21,23,24,26,27,29-33].
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic reviews that reported the integration of AI chatbots into diverse platforms, such as Slack (Slack Technologies, LLC), Messenger, WhatsApp, and Telegram [1,2,14], and the use of a large sample size, such as >100 participants in 10 of 15 studies [21,23,24,26,27,29-33].
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Discussion
|
This finding is aligned with many previous systematic reviews that reported that 4 of 9 studies were RCTs, remaining were quasiexperimental, feasibility, or pilot RCT studies [9], 2 of 10 studies were RCTs, majority were quasiexperimental [14], and 2 of 17 studies were RCTs, majority were quasiexperimental [1].
|
10.2196/16021
|
PMC7385637
|
PMC10007007
|
Discussion
|
This finding is aligned with many previous systematic reviews that reported that 4 of 9 studies were RCTs, remaining were quasiexperimental, feasibility, or pilot RCT studies [9], 2 of 10 studies were RCTs, majority were quasiexperimental [14], and 2 of 17 studies were RCTs, majority were quasiexperimental [1].
|
10.2196/20701
|
PMC7522733
|
PMC10007007
|
Discussion
|
This finding is aligned with many previous systematic reviews that reported that 4 of 9 studies were RCTs, remaining were quasiexperimental, feasibility, or pilot RCT studies [9], 2 of 10 studies were RCTs, majority were quasiexperimental [14], and 2 of 17 studies were RCTs, majority were quasiexperimental [1].
|
10.1093/jamia/ocy072
|
PMC6118869
|
PMC10007007
|
Discussion
|
These findings are consistent with many previous systematic reviews that reported moderate risk of outcomes from unintended sources owing to confounding in all quasiexperimental studies [9]; high risk of outcome measurement because evaluators were aware of the assigned intervention [8,9] or nonvalidated instruments were used for outcome measurement [1,11]; and moderate risk of bias in analysis owing to high attrition rate, the lack of analysis methods for bias correction, the lack of power analysis, and small sample size at follow-up [2,9].
|
10.2196/16021
|
PMC7385637
|
PMC10007007
|
Discussion
|
These findings are consistent with many previous systematic reviews that reported moderate risk of outcomes from unintended sources owing to confounding in all quasiexperimental studies [9]; high risk of outcome measurement because evaluators were aware of the assigned intervention [8,9] or nonvalidated instruments were used for outcome measurement [1,11]; and moderate risk of bias in analysis owing to high attrition rate, the lack of analysis methods for bias correction, the lack of power analysis, and small sample size at follow-up [2,9].
|
10.2196/14166
|
PMC6914342
|
PMC10007007
|
Discussion
|
These findings are consistent with many previous systematic reviews that reported moderate risk of outcomes from unintended sources owing to confounding in all quasiexperimental studies [9]; high risk of outcome measurement because evaluators were aware of the assigned intervention [8,9] or nonvalidated instruments were used for outcome measurement [1,11]; and moderate risk of bias in analysis owing to high attrition rate, the lack of analysis methods for bias correction, the lack of power analysis, and small sample size at follow-up [2,9].
|
10.1093/jamia/ocy072
|
PMC6118869
|
PMC10007007
|
Discussion
|
These findings are consistent with many previous systematic reviews that reported moderate risk of outcomes from unintended sources owing to confounding in all quasiexperimental studies [9]; high risk of outcome measurement because evaluators were aware of the assigned intervention [8,9] or nonvalidated instruments were used for outcome measurement [1,11]; and moderate risk of bias in analysis owing to high attrition rate, the lack of analysis methods for bias correction, the lack of power analysis, and small sample size at follow-up [2,9].
|
10.2196/jmir.7023
|
PMC5595406
|
PMC10007007
|
Discussion
|
These findings are consistent with many previous systematic reviews that reported moderate risk of outcomes from unintended sources owing to confounding in all quasiexperimental studies [9]; high risk of outcome measurement because evaluators were aware of the assigned intervention [8,9] or nonvalidated instruments were used for outcome measurement [1,11]; and moderate risk of bias in analysis owing to high attrition rate, the lack of analysis methods for bias correction, the lack of power analysis, and small sample size at follow-up [2,9].
|
10.1186/s12966-021-01224-6
|
PMC8665320
|
PMC10007007
|
Discussion
|
This finding is consistent with most of the previous systematic reviews that reported mixed findings on secondary outcome measures [1,2,7,9-11].
|
10.1093/jamia/ocy072
|
PMC6118869
|
PMC10007007
|
Discussion
|
This finding is consistent with most of the previous systematic reviews that reported mixed findings on secondary outcome measures [1,2,7,9-11].
|
10.1186/s12966-021-01224-6
|
PMC8665320
|
PMC10007007
|
Discussion
|
This finding is consistent with most of the previous systematic reviews that reported mixed findings on secondary outcome measures [1,2,7,9-11].
|
10.2196/20346
|
PMC7644372
|
PMC10007007
|
Discussion
|
This finding is consistent with most of the previous systematic reviews that reported mixed findings on secondary outcome measures [1,2,7,9-11].
|
10.2196/16021
|
PMC7385637
|
PMC10007007
|
Discussion
|
This finding is consistent with most of the previous systematic reviews that reported mixed findings on secondary outcome measures [1,2,7,9-11].
|
10.2196/jmir.7023
|
PMC5595406
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic literature review that reported inconsistent use of AI-software taxonomy and lack of depth of reported AI techniques and systems [14].
|
10.2196/20701
|
PMC7522733
|
PMC10007007
|
Discussion
|
In alignment with CONSORT-AI extension [20], future studies need to elaborate on the following components related to the AI algorithm: (1) the process of supplying input data to the AI algorithm, including the user interface that enables data collection, inclusion or exclusion criteria of input data, handling of unavailable data, and establishing the credibility of the data collected (eg, specifying the source of input data); (2) the output by the AI algorithm and its relevance to the health-related goals; (3) the AI functioning, including the type of personalization algorithm such as ML, NLP, etc, version of the AI algorithm, and the accuracy level of the algorithm; (4) performance backlogs in the AI algorithm deployed, which would indicate the level of safety in using AI algorithms, especially with vulnerable populations; (5) the level and type of expertise required to integrate and successfully deploy the AI algorithm; and (6) the skills needed by the participants to use the AI chatbot, which would indicate the number of resources required and the feasibility of using AI algorithms
|
10.1136/bmj.m3164
|
PMC7490784
|
PMC10007007
|
Discussion
|
These findings are consistent with the previous systematic literature reviews that reported that all the chatbot intervention studies were conducted in high-income countries [2,10,11,14], most studies were conducted with adults [2,7], and most studies did not focus on racial or ethnic minorities [2,14].
|
10.1186/s12966-021-01224-6
|
PMC8665320
|
PMC10007007
|
Discussion
|
These findings are consistent with the previous systematic literature reviews that reported that all the chatbot intervention studies were conducted in high-income countries [2,10,11,14], most studies were conducted with adults [2,7], and most studies did not focus on racial or ethnic minorities [2,14].
|
10.1177/0706743719828977
|
PMC6610568
|
PMC10007007
|
Discussion
|
These findings are consistent with the previous systematic literature reviews that reported that all the chatbot intervention studies were conducted in high-income countries [2,10,11,14], most studies were conducted with adults [2,7], and most studies did not focus on racial or ethnic minorities [2,14].
|
10.2196/jmir.7023
|
PMC5595406
|
PMC10007007
|
Discussion
|
These findings are consistent with the previous systematic literature reviews that reported that all the chatbot intervention studies were conducted in high-income countries [2,10,11,14], most studies were conducted with adults [2,7], and most studies did not focus on racial or ethnic minorities [2,14].
|
10.2196/20701
|
PMC7522733
|
PMC10007007
|
Discussion
|
These findings are consistent with the previous systematic literature reviews that reported that all the chatbot intervention studies were conducted in high-income countries [2,10,11,14], most studies were conducted with adults [2,7], and most studies did not focus on racial or ethnic minorities [2,14].
|
10.2196/20346
|
PMC7644372
|
PMC10007007
|
Discussion
|
The increased mobile connectivity and internet use in low-income countries [38] offer the potential to implement AI chatbot–based health behavior interventions.
|
10.3389/fdgth.2020.576361
|
PMC8521874
|
PMC10007007
|
Discussion
|
Similarly, with the rise in the use of smartphones and latest digital technologies among adolescents [40], AI chatbots offer the opportunity to deliver engaging behavioral health interventions to them.
|
10.3389/fpsyt.2020.606041
|
PMC7882508
|
PMC10007007
|
Discussion
|
The nonjudgmental and nonstigmatic attributes of AI chatbot–based interventions offer a solution to the challenges faced by adolescents in seeking behavioral health services, such as perceived and enacted stigma and lack of motivation [9,40].
|
10.2196/16021
|
PMC7385637
|
PMC10007007
|
Discussion
|
The nonjudgmental and nonstigmatic attributes of AI chatbot–based interventions offer a solution to the challenges faced by adolescents in seeking behavioral health services, such as perceived and enacted stigma and lack of motivation [9,40].
|
10.3389/fpsyt.2020.606041
|
PMC7882508
|
PMC10007007
|
Discussion
|
Only 7% (1/15) of studies, that is, the study by Maher et al [22], reported safety in terms of the absence of adverse events.
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic literature reviews that reported very few studies discussed participant safety or ethics in terms of adverse events [1,2,7,9] and data security or privacy [2,8].
|
10.1093/jamia/ocy072
|
PMC6118869
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic literature reviews that reported very few studies discussed participant safety or ethics in terms of adverse events [1,2,7,9] and data security or privacy [2,8].
|
10.1186/s12966-021-01224-6
|
PMC8665320
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic literature reviews that reported very few studies discussed participant safety or ethics in terms of adverse events [1,2,7,9] and data security or privacy [2,8].
|
10.2196/20346
|
PMC7644372
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic literature reviews that reported very few studies discussed participant safety or ethics in terms of adverse events [1,2,7,9] and data security or privacy [2,8].
|
10.2196/16021
|
PMC7385637
|
PMC10007007
|
Discussion
|
This finding is consistent with the previous systematic literature reviews that reported very few studies discussed participant safety or ethics in terms of adverse events [1,2,7,9] and data security or privacy [2,8].
|
10.2196/14166
|
PMC6914342
|
PMC10009463
|
Introduction
|
However, insulin resistance (IR) is considered the primary pathological basis for the associated reproductive dysfunction [1,2].
|
10.1210/er.2011-1034
|
PMC5393155
|
PMC10009463
|
Introduction
|
IR both promotes and interacts with hyperandrogenemia, which affects the function of the hypothalamic-pituitary-ovarian axis and causes abnormal follicular development [4].
|
10.1210/er.2015-1104
|
PMC5045492
|
PMC10009463
|
Introduction
|
Additionally, regardless of age, gestational diabetes mellitus, impaired glucose tolerance, and type 2 diabetes are all significantly more prevalent in patients with PCOS [5].
|
10.1093/humrep/dey256
|
PMC6112576
|
PMC10009463
|
Introduction
|
Metformin is often recommended to adult women or adolescents with PCOS or women with body mass index (BMI) > 25 kg/m2 for management of weight and metabolic disorders [5].
|
10.1093/humrep/dey256
|
PMC6112576
|
PMC10009463
|
Introduction
|
A recent meta-analysis also found that acupuncture can improve glucose metabolism and insulin sensitivity in patients with PCOS [10].
|
10.1155/2021/5555028
|
PMC8007365
|
PMC10009463
|
Methods
|
This analysis was performed strictly following the PRISMA statement [11].
|
10.1136/bmj.b2535
|
PMC2714657
|
PMC10009463
|
Results
|
Acupuncture was compared with sham acupuncture in five studies [[13], [14], [15], [16], [17]], acupuncture was compared with medicine in five studies [[18], [19], [20], [21], [22]], and acupuncture combined with medicine was compared with medicine alone in seven studies [[23], [24], [25], [26], [27], [28], [29]].
|
10.1093/humrep/deab272
|
PMC8888993
|
PMC10009463
|
Results
|
The assessment of allocation was described in three articles [13,19,22].
|
10.1093/humrep/deab272
|
PMC8888993
|
PMC10009463
|
Results
|
For blinding, three trials [13,19,23] were classified as “low risk” and the others as “unclear risk”.
|
10.1093/humrep/deab272
|
PMC8888993
|
PMC10009463
|
Results
|
Three articles [13,19,21] stated explicitly that third-party researchers processed the data.
|
10.1093/humrep/deab272
|
PMC8888993
|
PMC10009463
|
Results
|
Five studies [13,14,[19], [20], [21]] reported the loss of follow-up.
|
10.1093/humrep/deab272
|
PMC8888993
|
PMC10009463
|
Results
|
There was attrition bias in two of these studies [13,19] as a result of differences in the proportion of missing outcome data between the experimental and comparison groups.
|
10.1093/humrep/deab272
|
PMC8888993
|
PMC10009463
|
Results
|
4) than other treatments, with no significant between-study heterogeneity (I [2] = 38%).
|
10.1210/er.2011-1034
|
PMC5393155
|
PMC10009463
|
Results
|
4), with significant heterogeneity (I [2] = 82%).
|
10.1210/er.2011-1034
|
PMC5393155
|
PMC10009463
|
Discussion
|
There is also evidence that acupuncture can be an insulin sensitizer and may therefore contribute to controlling obesity and type 2 diabetes [32,33].
|
10.1038/nutd.2016.16
|
PMC4895377
|
PMC10009463
|
Discussion
|
Acupuncture ameliorates IR through enhancing autophagy [34], affecting insulin receptor signal transduction, and increasing the expression of insulin receptor substrates in the endometrium in a PCOS-like rat model [35,36].
|
10.1186/s10020-020-00198-8
|
PMC7374902
|
PMC10009463
|
Discussion
|
In obesity, inflammation, with increased accumulation and inflammatory polarization of immune cells, takes place in various tissues, including adipose tissue, pancreatic islet, and so on, which may contribute to obesity-linked metabolic dysfunctions, including insulin resistance and type 2 diabetes mellitus [37].
|
10.1161/CIRCRESAHA.119.315896
|
PMC7250139
|
PMC10009463
|
Discussion
|
In addition, sham acupuncture applied at non-acupuncture points may serve as an active control because acupoint areas can be enlarged by increased expression of nociceptive substances in individuals with painful conditions [46].
|
10.1186/s12906-017-1580-z
|
PMC5341424
|
PMC10009463
|
Discussion
|
It has therefore been suggested that more research into applying non-penetrating sham acupuncture at non-acupuncture points is necessary [47].
|
10.1136/bmj.m697
|
PMC7249245
|
PMC10009838
|
Background
|
It is characterized by many concerning epidemiological features like lower minimal infection dose resulting in higher transmissibility, immune evasion with the risk of reinfections and breakthrough infections, and an impaired response to COVID-19-specific treatment [3–5].
|
10.1128/spectrum.00926-22
|
PMC9430471
|
PMC10009838
|
Background
|
pharynx), which may lead to higher transmission rates and milder disease [5–8].
|
10.1016/j.xcrm.2022.100743
|
PMC9420712
|
PMC10009838
|
Background
|
Vaccine effectiveness (VE) against the wild-type virus and the Alpha, Beta and Delta variants was very high irrespective of outcome definition [9, 10].
|
10.1016/j.vaccine.2022.11.065
|
PMC9715487
|
PMC10009838
|
Background
|
Vaccine effectiveness (VE) against the wild-type virus and the Alpha, Beta and Delta variants was very high irrespective of outcome definition [9, 10].
|
10.1016/j.cmi.2021.10.005
|
PMC8548286
|
PMC10009838
|
Background
|
Previous studies showed decreased VE against the Omicron variant due to mutations in the spike protein, but effectiveness against severe disease remained high [11, 12].
|
10.1056/NEJMoa2203965
|
PMC9258753
|
PMC10009838
|
Background
|
Previous studies showed decreased VE against the Omicron variant due to mutations in the spike protein, but effectiveness against severe disease remained high [11, 12].
|
10.1056/NEJMoa2119451
|
PMC8908811
|
PMC10009838
|
Background
|
Initial data collected during the Delta wave were published recently [9].
|
10.1016/j.vaccine.2022.11.065
|
PMC9715487
|
PMC10009838
|
Methods
|
SARS-CoV-2 PCR and sequencing were performed at the Robert Koch Institute [13, 14] (for details, see supplement).
|
10.1186/s12985-021-01559-3
|
PMC8170437
|
PMC10009838
|
Methods
|
SARS-CoV-2 PCR and sequencing were performed at the Robert Koch Institute [13, 14] (for details, see supplement).
|
10.3389/fmicb.2021.651151
|
PMC8281033
|
PMC10009838
|
Methods
|
As the matched subgroup-analysis was not applicable for all subgroups due to insufficient number of patients in the strata, we primarily performed an unmatched analysis as also suggested by others [16].
|
10.1136/bmj.i969
|
PMC4770817
|
PMC10009838
|
Results
|
1).Table 1Characteristics of cases and controlsCasesControlsp-valuen276494Age (years) mean (SD)66.65 (16.18)65.25 (15.28)0.233Age group n (%) 18–59 years78 (28.3)150 (30.4)0.355 60–69 years55 (19.9)114 (23.1) 70–90 years143 (51.8)230 (46.6)Sex n (%) Male155 (56.2)291 (58.9)0.506 Female121 (43.8)203 (41.1)Highest school educational level n (%) No graduation16 (5.8)16 (3.2) 9 school years69 (25.0)113 (22.9)0.092 10 school years (secondary school certificate)128 (46.4)218 (44.1) 12 or 13 school years (high school graduation)63 (22.8)147 (29.8)BMI (kg/m2) mean (SD))26.93 (6.14)29.00 (18.11)0.066BMI group n (%) Underweight/normal weight (BMI ≤ 25 kg/m2)124 (44.9)188 (38.1)0.131 Overweight (BMI > 25-30 kg/m2)79 (28.6)171 (34.6) Obese (BMI > 30 kg/m2)73 (26.4)135 (27.3)Admission to intensive care unit (ICU) n (%) Yes23 (8.3)NA No253 (91.7)NADeath n (%) Yes9 (3.3)NA No267 (96.7)NAPre-existing comorbidities (general) n (%) < 3166 (60.1)350 (70.9)0.003 ≥ 3110 (39.9)144 (29.1)Pre-existing comorbidities (immune system) n (%) None191 (69.2)414 (83.8)< 0.001 ≥ 185 (30.8)80 (16.2)Vaccination (≥ 2 doses) n (%) Yes206 (74.6)461 (93.3) No70 (25.4)33 (6.7)<0.001Number of vaccine doses n (%) 057 (20.7)26 (5.3)< 0.001 113 (4.7)7 (1.4) 246 (16.7)49 (9.9) 3145 (52.5)350 (70.9) 415 (5.4)62 (12.6)Vaccine type n (%) One dose (mRNA or vector)13 (4.7)7 (1.4) Two doses (mRNA/mRNA)42 (15.2)41 (8.2) Two doses (vector/vector)1 (0.3)4 (0.8)Two doses (crossoverb)2 (0.7)4 (0.8)Three doses (mRNA/mRNA/mRNA)126 (45.6)281 (56.8) Three doses (mRNA/mRNA/vector)01 (0.0) Three doses (vector/vector/mRNA)10 (3.6)29 (5.8) Three doses (crossoverb/vector)1 (0.3)0 (0) Three doses (crossoverb/mRNA)7 (2.5)39 (7.8) Not vaccinated57 (20.6)26 (5.2) Four doses, other vaccine or missing information17 (6.1)62 (12.5)aHigh-risk comorbidities for severe course of COVID-19: diabetes type 2, Organ transplant, BMI > 30, COPD, renal insufficiency, heart failure[17]bfirst dose mRNA, second dose vector vaccine or vice versaFig.
|
10.1186/s12916-021-02058-6
|
PMC8390115
|
PMC10009838
|
Results
|
aHigh-risk comorbidities for severe course of COVID-19: diabetes type 2, Organ transplant, BMI > 30, COPD, renal insufficiency, heart failure[17]
|
10.1186/s12916-021-02058-6
|
PMC8390115
|
PMC10009838
|
Discussion
|
The proportion of older patients and cases with immunocompromising comorbidities in the Omicron wave was higher compared with our data from an earlier recruitment phase in the Delta wave [9].
|
10.1016/j.vaccine.2022.11.065
|
PMC9715487
|
PMC10009838
|
Discussion
|
This difference in clinical characteristics was confirmed in other studies [18, 19].
|
10.7717/peerj.13762
|
PMC9354737
|
PMC10009838
|
Discussion
|
However, the lower pathogenicity of the Omicron variant and the advanced therapeutic possibilities still led to milder disease in the Omicron wave compared with the Delta wave [9, 18–20].
|
10.1016/j.vaccine.2022.11.065
|
PMC9715487
|
PMC10009838
|
Discussion
|
However, the lower pathogenicity of the Omicron variant and the advanced therapeutic possibilities still led to milder disease in the Omicron wave compared with the Delta wave [9, 18–20].
|
10.1186/s12879-022-07781-w
|
PMC9610359
|
PMC10009838
|
Discussion
|
The proportion of unvaccinated cases and controls was markedly lower in the Omicron wave compared to the Delta wave [9] and the percentage of boostered cases and controls increased.
|
10.1016/j.vaccine.2022.11.065
|
PMC9715487
|
PMC10009838
|
Discussion
|
The results from the Omicron wave analyses were comparable with the results of other studies and systematic reviews [21–25].
|
10.3389/fimmu.2022.940562
|
PMC9449804
|
PMC10009838
|
Discussion
|
including 113 studies found a lower pooled VE for Omicron (two doses 56% and three doses 83%) compared to the VE against the Delta variant (85% and 93%, respectively) for COVID-19 related hospitalization [24].
|
10.1080/22221751.2022.2122582
|
PMC9542696
|
PMC10009838
|
Discussion
|
Recent studies using register data confirm that the 3-dose VE in the Omicron wave ranges between 80 % and 90 % [21, 22, 23]
|
10.1093/infdis/jiac161
|
PMC9129207
|
PMC10009838
|
Discussion
|
As other studies showed that protection against symptomatic infection decreases noticeably with increasing time lag after the last vaccination dose [22, 23, 25–27], this is an important finding.
|
10.1093/infdis/jiac161
|
PMC9129207
|
PMC10009838
|
Discussion
|
As other studies showed that protection against symptomatic infection decreases noticeably with increasing time lag after the last vaccination dose [22, 23, 25–27], this is an important finding.
|
10.3389/fimmu.2022.940562
|
PMC9449804
|
PMC10009838
|
Discussion
|
The comparison of three different methods of analysis showed the robustness of the results: the results of the pairwise-matched method were very similar to those of the non-matched analysis, but—as expected—not as precise [16].
|
10.1136/bmj.i969
|
PMC4770817
|
PMC10009838
|
Discussion
|
This might lead to a selection bias at a vaccine coverage greater than 85–90% [28, 29].
|
10.1016/j.vaccine.2017.04.035
|
PMC7008029
|
PMC10009838
|
Discussion
|
This might lead to a selection bias at a vaccine coverage greater than 85–90% [28, 29].
|
10.1016/j.vaccine.2017.04.037
|
PMC7007298
|
PMC10010178
|
Introduction
|
γ, the quarantine rate (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$U\rightarrow ^{\gamma }Q$\end{document}U→γQ); γϵ[0, 1].
|
10.1371/journal.pone.0263597
|
PMC8836351
|
PMC10010178
|
Introduction
|
and β, the confirmation rate of Q as C by mean of conventional method (e.g., laboratory diagnosis) or by subsequent additional tests, i.e., the total confirmation rate (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$Q\rightarrow ^{\beta }C$\end{document}Q→βC); βϵ[0, 1].
|
10.1371/journal.pone.0263597
|
PMC8836351
|
PMC10010178
|
Discussion
|
: although gamma is the quarantine rate, its values within the interval [0, 1] can account for the quality of the quarantine, and the more incomplete, partial, defective, or ineffective a quarantine regime is, the lower the gamma value.
|
10.1371/journal.pone.0263597
|
PMC8836351
|
PMC10010506
|
Background
|
Many machine-learning (ML) algorithms rely on dichotomous classification schemes where an algorithm determines the presence or absence of specific intracranial abnormalities such as intracranial hemorrhage, large vessel occlusion, or metastasis [1–4].
|
10.1148/radiol.2020191479
|
PMC8287889
|
PMC10010506
|
Background
|
For instance, more recently reported algorithms were shown to detect intracranial hemorrhages with specificities of 0.93–0.95, sensitivities of 0.87–0.96, positive predictive values of 0.71–0.96, and negative predictive values of 0.95–0.95 [4–6].
|
10.3174/ajnr.A6926
|
PMC7872180
|
PMC10010506
|
Background
|
With the expansion of real-life applications of various ML algorithms and the growing need for safety, the recognition and appropriate handling of uncertainty is becoming critical [8, 9].
|
10.1038/s41746-020-00367-3
|
PMC7785732
|
PMC10010506
|
Discussion
|
Following the suggestions by Kompa and colleagues [9], we incorporated uncertainty into an ML algorithm that analyzes head CT scans.
|
10.1038/s41746-020-00367-3
|
PMC7785732
|
PMC10010506
|
Discussion
|
In addition, early ischemia may not create CT scan abnormalities that are detectable by even experienced neuroradiologists, even though ischemic stroke may produce severe neurological deficits [21, 22].
|
10.1371/journal.pone.0176622
|
PMC5435168
|
PMC10010506
|
Discussion
|
In addition, early ischemia may not create CT scan abnormalities that are detectable by even experienced neuroradiologists, even though ischemic stroke may produce severe neurological deficits [21, 22].
|
10.1038/s41598-023-27496-5
|
PMC9814956
|
PMC10010506
|
Discussion
|
Our group has created and validated such an algorithm [22].
|
10.1038/s41598-023-27496-5
|
PMC9814956
|
PMC10010506
|
Discussion
|
Other ML algorithms have been shown to reduce the turnaround time for the identification and interpretation of intracranial hemorrhage or other urgent intracranial abnormalities such as intracranial hemorrhage on head CTs [23].
|
10.1148/ryai.2020200024
|
PMC8043365
|
PMC10010521
|
Introduction
|
The spread of Shigella can also occur via flies, which act as a mechanical vector for the organism, however this mode of transmission has not been widely reported in Australia [5,6].
|
10.1371/journal.pntd.0002280
|
PMC3688559
|
PMC10010521
|
Introduction
|
Shigella have a low infectious dose between 10 to 100 organisms, and the clinical presentations of infection can vary from mild watery diarrhoea to severe dysentery (bloody diarrhoea) compounded by systemic complications such as electrolyte imbalance, seizures, fever, nausea and haemolytic uraemic syndrome [7].
|
10.1007/s40475-014-0019-6
|
PMC4126259
|
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