Evaluation of the Feasibility and Efficacy of Point-of-Care Antibody Tests for Biomarker-Guided Management of Coronavirus Disease 2019
Bibliographic record
Abstract
BACKGROUND: Biomarker-guided therapy could improve management of inpatients with coronavirus disease 2019 (COVID-19). Although some results indicate that antibody tests are prognostic, little is known about patient management using point-of-care (POC) antibody tests. METHODS: COVID-19 inpatients were recruited to evaluate 2 POC tests: LumiraDx and RightSign. Ease of use data were collected. Blood was also collected for centralized testing using an established antibody assay (GenScript cPass). A nested case-control study assessed if POC tests conducted on stored specimens were predictive of time to sustained recovery, mortality, and a composite safety outcome. RESULTS: While both POC tests exhibited moderate agreement with the GenScript assay (both agreeing with 89% of antibody determinations), they were significantly different from the GenScript assay. Treating the GenScript assay as the gold standard, the LumiraDx assay had 99.5% sensitivity and 58.1% specificity whereas the RightSign assay had 89.5% sensitivity and 84.0% specificity. The LumiraDx assay frequently gave indeterminant results. Both tests were significantly associated with clinical outcomes. CONCLUSIONS: Although both POC tests deviated moderately from the GenScript assay, they predicted outcomes of interest. The RightSign test was easier to use and was more likely to detect those lacking antibody compared to the LumiraDx test treating GenScript as the gold standard. CLINICAL TRIALS REGISTRATION: NCT05227404.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".