313 Dermatome-Induced Lacerations: Incidence, Management, and Preventive Measures
Bibliographic record
Abstract
Dermatome-induced lacerations are a known complication; however, there is a paucity of literature discussing the incidence and predisposing factors. The aim of this study was to determine the incidence and risk factors in order to develop a preventive algorithm. An 18-question survey was sent to all U.S. and Canadian burn unit directors. Surgeons were queried about type and location of their practices, average annual caseload of skin graft harvesting, and number of dermatome-induced lacerations. The survey also asked about donor site location, harvesting technique and equipment, laceration severity, and causative factors. An algorithm was developed based on the results and the current manufacturers’ guidelines. Fifty-six responses (42% response rate) were received from the burn unit directors. They reported 133 lacerations over the past 5 years. The overall incidence of dermatome-induced lacerations was 0.1% per year (1.3 per 1,000 cases). The most commonly attributed causes were excessive pressure (25.0%) and patient factors (18.4%). Most lacerations occurred when using air dermatomes (73.0%) with a 4-inch guard (63.5%), 0.010-0.015-inch thickness (78.4%), and 30–45° angulation (47.3%); the most common brand was Zimmer (71.6%). The dermatome was typically set up by a scrub tech/nurse (48.6%), while the skin harvesting was performed by residents (39.2%) or attendings (35.1%). Lacerations typically extended to subcutaneous tissue (70.3%), with no neurovascular injury (86.5%). Our study showed that dermatome-induced lacerations are rare events and that certain factors predispose patients to injury. An algorithm was developed to provide guidance on risk factor identification and the set up and use of dermatomes. This study provides the only evidence on the incidence, etiology and risk factors of dermatome-induced lacerations. The development of an algorithm will help to increase the safety of skin grafting, the most commonly performed procedure in burn surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".