Asymptomatic testing people for SARS-CoV-2 in healthcare facilities: A systematic review
Bibliographic record
Abstract
Background: Asymptomatic testing involves the process whereby individuals who do not show symptoms of COVID-19 are tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using any of the available laboratory test techniques. Aim: To evaluate the effectiveness of testing asymptomatic individuals visiting, living or working in healthcare facilities in reducing SARS-CoV-2 viral infections. Setting: Healthcare databases. Method: Electronic databases were searched and limited to English language and studies published 2020 to 02 September 2022. Following the methods for rapid systematic reviews, data were analysed using a fixed effect model, and results of the effect estimate were reported as odds ratios (OR) with their confidence intervals (CI) (95% CI). Results: Databases’ searches yielded 3065 articles after deduplication and 3 studies by searching reference lists of included articles. After screening abstracts and full text articles, 3 cohort studies were included, each with serious risk of bias. Very low certainty evidence shows a decrease in occurrence of SARS-CoV-2 infections in the asymptomatic testing group among patients going for index surgery (OR: 0.05, 95 % CI: 0.00–0.82; 501 participants; 1 study) and among long term care facility staff (OR: 0.31, 95 % CI: 0.18–0.52; 3457 participants; 2 studies, I2 = 89%) than the ‘no asymptomatic testing’ group. However, its effect on their residents was contradictory. Conclusion: There is limited quality evidence to support asymptomatic testing of individuals for SARS-CoV-2 in the prevention of virus transmission in health care settings. Contribution: In the event of a future pandemic, this review offers current evidence on the potential effects of asymptomatic testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".