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
BACKGROUND: Rapid IgM/IgG antibody tests were largely used in lieu of RT-PCR tests as part of COVID-19 public health response activities in Lima, Peru. To assess their utility, we explored the relationship between the time since onset of several COVID-19-related symptoms and the sensitivity of a rapid combined IgM/IgG antibody test. METHODS: We collected data from a community sample of individuals (n = 492) who received concurrent RT-PCR and rapid IgM/IgG antibody testing between May 2020 and March 2021. We estimated the sensitivity of the antibody test, against the RT-PCR test, by weeks since symptom onset via segmented regression analysis. RESULTS: The overall sensitivity of the rapid IgM/IgG antibody test was 46.7% (95% CI, 42.4-51.2%). Among 372 (75.6%) participants who reported COVID-19-related symptoms, sensitivity increased from 30.4% (95% CI, 24.7-36.6%) in week 1 after symptom onset to 83.3% (95% CI, 41.6-98.4%) in week 4. The test sensitivity increased by 31.9% (95% CI, 24.8-39.0%) per week until week 2 to 3, then decreased by - 6.0% (95% CI, - 25.7-13.7%) per week thereafter. CONCLUSION: Rapid antibody tests are a poor substitute for RT-PCR testing, regardless of presenting symptoms. This highlights the need for future pandemic planning to include timely and equitable access to gold-standard diagnostics, treatment, and vaccination.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".