Post-exposure testing at healthcare facilities with SARS-CoV-2 transmission: A rapid review
Bibliographic record
Abstract
Background: Post-exposure severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing following health facility outbreaks may control the spread of infection. Aim: This study aimed to assess the impact of testing for SARS-CoV-2 infection on health outcomes during healthcare facility outbreaks. Setting: This review included studies conducted at skilled nursing facilities, a cancer centre, and a geriatric psychiatric facility. Methods: We followed the methods for conducting rapid systematic reviews, searched databases from December 2019 to August 2022, assessed the risk of bias using the modified Newcastle Ottawa scale, and graded the certainty of evidence using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. We pooled the prevalence, mortality, and hospitalisation results as appropriate. Results: Of the 3055 articles from database search, no study was eligible for inclusion as outlined in the protocol. However, eight non-comparative reports (case series) in skilled nursing facilities were included. The pooled prevalence of SARS-CoV-2 infection among residents of care homes and patients were 38% (95% confidence interval [CI] = 25% – 51%; 5 studies, 2044 participants; I2 = 94%, very low certainty evidence) and was 12% (95% CI = 6% – 19%; 5 studies, 2312 participants; I2 = 94%, very low certainty evidence) for exposed healthcare workers. The pooled mortality estimate and hospitalisation rate were 17% and 24%, respectively, (very low certainty evidence). Conclusion: There is no identified evidence for or against testing of people in healthcare facilities where there is ongoing transmission of SARS-CoV-2 infection. Contribution: The evaluation of the effectiveness of testing strategies during SARS-CoV-2 outbreaks need baseline and follow-up data from well-designed before and after studies appropriate for the setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".