Implementation of facemask sampling for the detection of infectious individuals with SARS-CoV-2 in high stakes clinical examinations – a feasibility study
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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