Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to examine the difference in self-contamination rates and levels of contact and droplet protection associated with enhanced respiratory and contact precautions (E-RCP) and a personal protective system that included a full body suit, personal protective equipment and a powered air-purifying respirator (PAPR). METHODS: In this prospective, randomized, controlled crossover study, 50 participants donned and removed E-RCP and PAPR in random order. Surrogate contamination consisted of fluorescein solution and ultraviolet (UV) light- detectable paste, which was applied after each ensemble was donned. A blinded evaluator inspected participants for contamination using a UV lamp after removal of each ensemble. Areas of contamination were counted and measured in square centimetres. Donning and removal violations were recorded. The primary end point was the presence of any contamination on the skin or base clothing layer. RESULTS: Participants wearing E-RCP were more likely to experience skin and base-clothing contamination; their contamination episodes measuring > or = 1 cm2 were more frequent, and they had larger total areas of contamination (all p < 0.0001). The anterior neck, forearms, wrists and hands were the likeliest zones for contamination. Participants donning PAPR committed more donning procedure violations (p = 0.0034). Donning and removing the PAPR system took longer than donning and removing E-RCP garments (p < 0.0001). INTERPRETATION: Participants wearing E-RCP were more likely to experience contamination of their skin and base clothing layer. Those wearing PAPR required significantly more time to don and remove the ensemble and violated donning procedures more frequently.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".