Hands-Free Technique in the Operating Room: Reduction in Body Fluid Exposure and the Value of a Training Video
Bibliographic record
Abstract
OBJECTIVES: This study sought to determine if (1) using a hands-free technique (HFT)--whereby no two surgical team members touch the same sharp item simultaneously--> or = 75% of the time reduced the rate of percutaneous injury, glove tear, and contamination (incidents); and (2) if a video-based intervention increased HFT use to > or = 75%, immediately and over time. METHODS: During three and four periods, in three intervention and three control hospitals, respectively, nurses recorded incidents, percentage of HFT use, and other information in 10,596 surgeries. The video was shown in intervention hospitals between Periods 1 and 2, and in control hospitals between Periods 3 and 4. HFT, considered used when > or = 75% passes were done hands-free, was practiced in 35% of all surgeries. We applied logistic regression to (1) estimate the rate reduction for incidents in surgeries when the HFT was used and not used, while adjusting for potential risk factors, and (2) estimate HFT use of about 75% and 100%, in intervention compared with control hospitals, in Period 2 compared with Period 1, and Period 3 compared with Period 2. RESULTS: A total of 202 incidents (49 injuries, 125 glove tears, and 28 contaminations) were reported. Adjusted for differences in surgical type, length, emergency status, blood loss, time of day, and number of personnel present for > or = 75% of the surgery, the HFT-associated reduction in rate was 35%. An increase in use of HFT of > or = 75% was significantly greater in intervention hospitals, during the first post-intervention period, and was sustained five months later. CONCLUSION: The use of HFT and the HFT video were both found to be effective.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".