Use of safety scalpels and other safety practices to reduce sharps injury in the operating room: What is the evidence?
Bibliographic record
Abstract
BACKGROUND: The occupational hazard associated with percutaneous injury in the operating room (OR) has encouraged harm reduction through behaviour change and the use of safety-engineered surgical sharps. Some Canadian regulatory agencies have mandated the use of "safety scalpels." Our primary objective was to determine whether safety scalpels reduce the risk of percutaneous injury in the OR, while a secondary objective was to evaluate risk reduction associated with other safety practices. METHODS: We used evidence review methods described by the International Liaison Committee on Resuscitation and conducted a systematic, English-language search of Ovid, MEDLINE and EMBASE using the following search terms: "safety-engineered scalpel," "mistake proofing device," "retractable/removable blade/scalpel," "pass tray," "hands free passing," "neutral zone," "sharpless surgery," "double/cutproof gloving" and "blunt suture needles." Included articles were scored according to level of evidence; quality; and whether they were supportive, opposed or neutral to the study question(s). RESULTS: Of 72 included citations, none was supportive of the use of safety scalpels. There was high-level/quality evidence (Cochrane reviews) in support of risk reduction through double-gloving and use of blunt suture needles, with additional evidence supporting a pass tray/neutral zone for sharps handling (4 of 5 articles supportive) and use of suturing adjuncts (1 article supportive). CONCLUSION: There is insufficient evidence to support regulated use of safety scalpels. Injury-reduction strategies should emphasize proven methods, including double-gloving, blunt suture needles and use of hands-free sharps transfer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".