Multi-centre post-implementation evaluation of SARS-CoV-2 antigen-based point of care tests used for asymptomatic screening of continuing care healthcare workers
Bibliographic record
Abstract
Abstract OBJECTIVES Frequent screening of SARS-CoV-2 among asymptomatic populations using antigen-based point of care tests (APOCT) is occurring globally with limited clinical performance data. The positive predictive value (PPV) of two APOCT used in the asymptomatic screening of SARS-CoV-2 among healthcare workers (HCW) at continuing care (CC) sites across Alberta, Canada was evaluated. METHODS Between February 22 and May 2, 2021, CC sites implemented SARS-CoV-2 voluntary screening of their asymptomatic HCW. Onsite testing with Abbott Panbio or BD Veritor occurred on a weekly or twice weekly basis. Positive APOCT were confirmed with a real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) reference method. RESULTS A total of 71,847 APOCT (17,689 Veritor and 54,158 Panbio) were performed among 369 CC sites. Eighty-seven (0.12%) APOCT were positive, of which 39 (0.05%) confirmed as true positives using rRT-PCR. Use of the Veritor and Panbio resulted in a 76.6% and 30.0% false positive detection, respectively (p<0.001). This corresponded to a 23.4% and 70.0% PPV for the Veritor and Panbio, respectively. CONCLUSIONS Frequent screening of SARS-CoV-2 among asymptomatic HCW in CC, using APOCT, resulted in a very low detection rate and a high detection of false positives. Careful assessment between the risks vs benefits of APOCT programs in this population needs to be thoroughly considered before implementation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".