Efficacy of a Novel Intraoperative Engineered Sharps Injury Prevention Device: Pilot Usability and Efficacy Trial
Bibliographic record
Abstract
Background The American College of Surgeons reports 88,320 intraoperative needlestick injuries (NSIs) per year, resulting in US $376 to US $2456 in costs per NSI. Engineered sharps injury prevention (ESIP) devices protect against NSIs. To our knowledge, no study has been published to date to demonstrate clinical effectiveness of an intraoperative ESIP device. Operative Armour is a wearable arm cuff that can be donned during surgical closure to allow surgeons to keep a suture pack and sharps protection container on their forearm. Objective We characterize Operative Armour’s ESIP device effectiveness in a tertiary hospital, hypothesizing that this device will decrease NSI risk by decreasing behaviors associated with NSIs: needle passing and handling. Methods A prospective case-control study was conducted with institutional review board quality improvement designation in which authors observed skin closures of plastic surgery procedures. To ensure accuracy, one surgeon was observed at a time. Control surgeries were purely observational; intervention cases involved surgeon use of the device during skin closure. Outcomes of interest included needle passing, needle handling, lost needles, and loaded waiting needles. Results Surgeons were observed in 50 control and 50 intervention cases. Operative Armour eliminated needle passing during skin closure. One NSI occurred in one control case; no NSIs were observed in intervention cases (P=.36). The mean number of loaded and unprotected waiting needles was also significantly decreased in the intervention group from 2.3 to 0.2 (P<.001). Furthermore, a multivariable linear regression established that Operative Armour significantly decreased the number of needle adjustments by hand per stitch observed (F4, 21.68=3.72; P=.01). In fact, needle adjustments by hand decreased overall (1 adjustment per 10 stitches vs 1 adjustment per 5 stitches, P=.004), and adjustments occurred half as frequently with use of Operative Armour in free flap reconstruction (1 adjustment per 10 stitches vs 1 adjustment per 5 stitches, P=.03) and a quarter as frequently in other breast reconstruction cases such as mastopexy (1 adjustment per 20 stitches vs 1 adjustment per 5 stitches, P=.002). Conclusions Operative Armour effectively functions as an ESIP device by decreasing intraoperative needle passing and handling. Although sample size prohibits demonstrating a decrease in NSIs during observed cases, by decreasing behaviors that drive NSI risk, we anticipate an associated decrease in NSIs with use of the device.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".