Preventing Respiratory Viral Illness Invisibly (PRiVII): protocol for a pragmatic cluster randomized trial evaluating far-UVC light devices in long-term care facilities to reduce infections
Bibliographic record
Abstract
Abstract Background Respiratory viral illness (RVI)—e.g., influenza, COVID-19—is a serious threat in long-term care (LTC) facilities. Standard infection control measures are suboptimal in LTC facilities because of residents’ cognitive impairments, care needs, and susceptibility to loneliness and mental illness. Further, LTC residents living with high degrees of frailty who contract RVIs often develop the so-called atypical symptoms (e.g., delirium, worse mobility) instead of typical cough and fever, delaying infection diagnosis and treatment. Although far-UVC (222 nm) light devices have shown potent antiviral activity in vitro, clinical efficacy remains unproven. Methods Following a study to assay acceptability at each site, this multicenter, double-blinded, cluster-randomized, placebo-controlled trial aims to assess whether far-UVC light devices impact the incidence of RVIs in LTC facilities. Neighborhoods within LTC facilities are randomized to receive far-UVC light devices (222 nm) or identical placebo light devices that emit only visible spectrum light (400–700 nm) in common areas. All residents are monitored for RVIs using both a standard screening protocol and a novel screening protocol that target atypical symptoms. The 3-year incidence of RVIs will be compared using intention-to-treat analysis. A cost-consequence analysis will follow. Discussion This trial aims to inform decisions about whether to implement far-UVC light in LTC facilities for RVI prevention. The trial design features align with this pragmatic intent. Appropriate additional ethical protections have been implemented to mitigate participant vulnerabilities that arise from conducting this study. Knowledge dissemination will be supported through media engagement, peer-reviewed presentations, and publications. Trial registration ClinicalTrials.gov NCT05084898. October 20, 2021.
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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 | 0.034 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.010 |
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".