Impact of nasal photodisinfection on SARS-CoV-2 infection in an industrial workplace
Bibliographic record
Abstract
Objectives: We aimed to evaluate a quality improvement initiative designed to control SARS-CoV-2 (COVID) using the large-scale deployment of antimicrobial photodisinfection therapy (aPDT) for nasal decolonization in a Canadian industrial workplace (a food processing plant). Study design: Using a retrospective chart review of treatment questionnaires, linked to COVID laboratory testing results, a quality improvement assessment was analyzed to determine treatment effectiveness and safety. Methods: This voluntary aPDT intervention involved the administration of a light-sensitive liquid to the nose followed by nonthermal red-light irradiation on a weekly basis. Employees in food processing industries are at increased risk for COVID infection due to the nature of their work environments. In an effort to mitigate the transmission and consequences of the disease among such workers and the community at large, aPDT was added to a well-established bundle of pre-existing pandemic safety measures (e.g., mask-wearing, testing, contact tracing, workplace-engineered barriers, increased paid sick leave). Results: From December 2020 to May 2021, we found high interest in and compliance with aPDT treatment, along with a statistically significant lower PCR test positivity rate in the study population in comparison to the case rates for the local Canadian province. Treatment safety monitoring and outcomes of the aPDT program demonstrated no serious adverse events. Conclusions: This study suggests nasal photodisinfection provides safe and effective COVID viral suppression when deployed across the majority of workers in an industrial workplace setting.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".