Breath collection protocol for SARS-CoV-2 testing in an ambulatory setting
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
Abstract Background . The SARS-CoV-2 pandemic changed the way the society functioned. The race to develop a rapid, non-invasive, widely available test resulted in multiple studies examining the potential of breath to be that ‘game changing test’. Breath sampling is a non-invasive point of care test, but SAR-CoV-2 has introduced a level of danger into collection and analysis that requires a change in workflow to keep staff and participants safe. We developed a SARS-CoV 2 breath test work flow for collection and processing of breath samples in an ambulatory care setting and prospectively evaluated the protocol. Protocol development included testing the effect of respiratory filters on the integrity and reproducibility of breath samples. Methods . Prospective, observational study conducted at community COVID-19 testing sites, collecting breath samples from patients presenting for RT-PCR testing. Breath was collected via Tedlar®, and/or BioVOC-2™ as well as an environmental sample for all participants. Samples were transferred to Tenex tubes, dry purged and analyzed using a Centri automated sample introduction machine, GC, and a Bench-ToF-HD. Results . We successfully collected and processed 528 breath samples from 393 participants at community-based ambulatory COVID-19 test sites. The majority of samples were collected before vaccines were available and throughout the emergence of the Delta Variant. No staff member was infected. Conclusion . We demonstrated a safe workflow for the collection, handling, transport, storage, and analysis of breath samples during the pandemic collecting highly infectious SARS-CoV-2 positive breath samples. This was done without filters as they added complexity to the breath matrix, jeopardizing the sample integrity.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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