Additional file 1 of Low detection rate of RT-PCR-confirmed COVID-19 using IgM/IgG rapid antibody tests in a large community sample in Lima, Peru
Bibliographic record
Abstract
Additional file 1. Table S1. Reported symptoms of all participants with sample collected (and tested) for serologic antibody test on same day as sample that resulted in RT-PCR positive for SARS-CoV-2 (n=492); no symptom duration outliers excluded. Table S2. Reported symptoms of all participants with sample collected (and tested) for serologic antibody test on same day as sample that resulted in RT-PCR positive for SARS-CoV-2 (n=492); mild and extreme symptom duration outliers excluded. Table S3. Sensitivity of the rapid antibody test# by symptom duration and outlier exclusion criteria. Fig. S1. Plots of antibody test sensitivity against the number of weeks since onset of symptoms among those who reported at least one symptom before testing, using the RT-PCR test as the reference, stratified by the types of outliers excluded in the analysis: extreme outliers, which were defined as persons whose time since symptom onset was longer than the third quartile plus 3 times the interquartile range (IQR, defined as the difference between the first and third quartile of the reported times since onset for a given symptom); mild outliers were those beyond the third quartile plus 1.5 times the IQR); or no outliers excluded. The circles show the crude estimated sensitivity and the error bars show the Wilson’s score 95% confidence intervals. The plotted lines are the fitted segmented linear regressions for the rapid antibody tests. Table S4. Segmented regression analyses of antibody test sensitivity by weeks since symptom onset, by symptom duration outlier exclusion criteria. Table S5. Overall antibody test sensitivity* per week since symptom onset for common symptoms. Table S6.Segmented regression analyses of antibody test sensitivity versus weeks since symptom onset, by symptom.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.662 | 0.044 |
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".