Bacterial contamination of surgical loupes and headlights
Bibliographic record
Abstract
BACKGROUND: Medical equipment can transmit pathogenic bacteria to patients. This single-institution point prevalence study aimed to characterise the types and relative amount of bacteria found on surgical loupes, headlights and their battery packs. METHOD: Surgical loupes, headlights and battery packs of 16 otolaryngology staff and residents were sampled, cultured and quantified. Plate scores were summed for each equipment type, and the total was divided by the number of users to generate mean bacterial burden scores. Residents completed a questionnaire regarding their equipment cleaning practices. RESULTS: The contamination rates of loupes, headlights and battery packs were 68.75 per cent, 100 per cent and 75 per cent, respectively. Battery packs cultured more bacteria (1.58 per swab ± 1.00) than loupes (0.75 per swab ± 0.66; p = 0.024). Headlights had non-significantly greater growth (1.50 per swab ± 0.71) than loupes (p = 0.052). Bacterial growth was significantly higher from inner surfaces of loupes (p = 0.035) and headlights (p = 0.037). Potentially pathogenic bacteria were cultured from the equipment of five participants, including: Pantoea agglomerans, Acinetobacter radioresistens, Staphylococcus aureus, Acinetobacter calcoaceticus baumannii complex and Moraxella osloensis. CONCLUSION: This study demonstrates that surgical loupes and headlights used in otolaryngology harbour non-pathogenic skin flora and potentially pathogenic bacteria.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".