Implementation of facemask sampling for the detection of infectious individuals with SARS-CoV-2 in high stakes clinical examinations – a feasibility study
Bibliographic record
Abstract
Introduction: SARS-CoV-2 may transmit across vaccinated cohorts during practical clinical examinations. We sought to assess the feasibility of facemask sampling (FMS) to identify individuals emitting SARS-CoV-2 during a mock PACES exam. Methods: In May 2022 we recruited participants from a mock PACES examination in Leicester, UK. Following a negative lateral flow test assay, all participants wore modified facemasks able to capture exhaled virus during the assessment (FMS). A concomitant upper respiratory tract sample (URTS) was provided prior to FMS. Exposed facemasks were processed by removal and dissolution of sampling matrices fixed within the mask and cycle thresholds values quantified by RT-qPCR. Participants were asked to grade statements regarding the comfort, effort, ethics and communication when providing FMS; laboratory technicians were asked to grade key statements surrounding suitability of samples for processing. Results: 34 participants provided concomitant URTS and FMS during the examination. One participant was positive for SARS-CoV-2, with a cycle threshold value of 22.5 on URTS, but negative (no viral RNA detected) on FMS; no transmission to others was identified from this individual. Participants responded positively to statements regarding FMS describing all four domains; however, 69% of participants felt that a positive result from FMS alone was insufficient for diagnosis and that further tests were required. All but one FMS sample was suitable for processing. Discussion: FMS during PACES exams are acceptable among participants and samples provided are suitable for processing. Our results demonstrate feasibility of FMS within practical examination settings and support the further assessment of FMS as a scalable tool that can be compared with URTS to identify those who are infectious.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".