Evaluation of a home-based 7-day infection control strategy for healthcare workers following high-risk exposure to SARS-CoV-2: a cohort study
Bibliographic record
Abstract
ABSTRACT Background Evidence-based infection control strategies are needed for healthcare workers (HCWs) following high-risk exposure to SARS-CoV-2. This study evaluated the negative predictive value (NPV) of a home-based 7-day infection control strategy. Methods HCWs advised by their Infection Control or Occupational Health officer to self-isolate due to a high-risk SARS-CoV-2 exposure were enrolled between May-September 2020. The strategy consisted of symptom-triggered nasopharyngeal SARS-CoV-2 RNA testing from day 0-6 post exposure, followed by standardized home-based nasopharyngeal swab and saliva testing on day 7. The NPV of this strategy was calculated for i) clinical COVID-19 diagnosis from day 8-14 post exposure, and for ii) asymptomatic SARS-CoV-2 detected by standardized nasopharyngeal swab and saliva specimens collected at days 9-10 and 14 post exposure. Interim results are reported in the context of a second wave threatening this essential workforce. Results Among 30 HCWs enrolled to date (age 31±9 years, 24 [80.0%] female), 3 were diagnosed with COVID-19 by day 14 post exposure (secondary attack rate 10.0%), with all cases detected by the 7-day infection control strategy: NPV for subsequent clinical COVID-19 or asymptomatic SARS-CoV-2 detection by day 14 was 100.0% (95%CI: 93.1-100.0%). Interpretation Among HCWs with high-risk exposure to SARS-CoV-2, a home-based 7-day infection control strategy may have a high NPV for subsequent COVID-19 and asymptomatic SARS-CoV-2 detection. While ongoing data collection and data sharing are needed to improve the precision of the estimated NPV, we report interim results to inform infection control strategies in light of a second wave threatening this essential workforce.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".