Questionnaire‐based vs universal PCR testing for SARS‐CoV‐2 in women admitted for delivery
Bibliographic record
Abstract
BACKGROUND: It has been suggested that women admitted for delivery should have universal PCR testing for SARS-CoV-2. Yet, the considerable difference in the incidence of COVID-19 between different geographic regions may affect screening strategies. Therefore, we aimed to compare questionnaire-based testing versus universal PCR testing for SARS-CoV-2 in women admitted for delivery. METHODS: A prospective cohort study of women admitted for delivery at a single center during a four-week period (April 22-May 25, 2020). All women completed a questionnaire about COVID-19 signs, symptoms, or risk factors, and a nasopharyngeal swab for PCR for SARS-CoV-2. Women who were flagged as suspected COVID-19 by the questionnaire (questionnaire-positive) were compared with women who were not flagged by the questionnaire (questionnaire-negative). RESULTS: Overall, 446 women were eligible for analysis, of which 54 (12.1%) were questionnaire-positive. PCR swab detected SARS-CoV-2 in four (0.9%) women: 3 of 392 (0.8%) in the questionnaire-negative group, and 1 of 54 (1.9%) in the questionnaire-positive group (P = .43), yielding a number needed to screen of 92 (95% CI 62-177). In 96% of the cases, the PCR results were obtained only in the postpartum period. No positive PCR results were obtained from neonatal testing for SARS-CoV-2. The sensitivity of the questionnaire was 75.0%, and the negative predictive value was 99.7%. CONCLUSIONS: Although the rate of positive PCR results was not significantly different between the groups, the number needed to screen is considerably high. The use of questionnaire-based PCR testing in areas with low incidence of COVID-19 allows for a reasonable allocation of resources and is easy to implement.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".