SARS-CoV-2 serology: Validation of high-throughput chemiluminescent immunoassay (CLIA) platforms and a field study in British Columbia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: SARS-CoV-2 antibody testing is required for estimating population seroprevalence and vaccine response studies. It may also increase case identification when used as an adjunct to routine molecular testing. We performed a validation study and evaluated the use of automated high-throughput assays in a field study of COVID-19-affected care facilities. METHODS: Anti-SARS-CoV-2. The validation study included 107 samples (42 known positive; 65 presumed negative). The field study included 296 samples (92 PCR positive; 204 PCR negative or not PCR tested). All samples were tested by the six assays. RESULTS: All assays had sensitivities >90% in the field study, while in the validation study, 5/6 assays were >90% sensitive and DiaSorin was 79% sensitive. Specificities and negative predictive values were >95% for all assays. Field study estimated positive predictive values at 1-10% disease prevalence were 100% for Siemens, Abbott and Roche, while DiaSorin and Ortho assays had lower PPVs at 1% prevalence, but PPVs increased at 5-10% prevalence. In the field study, addition of serology increased diagnoses by 16% compared to PCR testing alone. CONCLUSIONS: All assays evaluated in this study demonstrated high sensitivity and specificity for samples collected at least 14 days post-symptom onset, while sensitivity was variable 0-14 days after infection. The addition of serology to the outbreak investigations increased case detection by 16%.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it