The effect of Steri-Strip orientation on wound healing: A survey of current applications and trends amongst surgeons
Bibliographic record
Abstract
Wound closure strips are used in the repair of superficial, low tension wounds, lacerations, and surgical incisions.They represent an inexpensive, easy, and painless way to close such wounds or as an adjunct in a multi-layered closure. There is debate over correct orientation (transverse, oblique, zig-zag or parallel) and importance of wound closure strips relative to the incision.The purpose of this research was to examine the closure techniques amongst Canadian plastic surgeons and current applications of wound closure strips using an online survey circulated to members of the Canadian Society of Plastic Surgery. Participants answered a series of multiple choice and short answer questions regarding their practice,closure techniques and use of wound closure strips.A total of 120 plastic surgeons (30.0%) completed the survey with 89.9% reporting wound closure strips usage. Of respondents who used wound closure strips, 57.2% used them in more than half of all wound closures. Parallel orientation to the wound (52.3%) and per- pendicular orientation (30.8%) were the most common techniques employed, and these were applied in a consistent fashion (90.6%) covering the whole wound (78.1%). Most plastic surgeons agreed there is a need to further study adhesive strips.This survey of Canadian plastic surgeons has demonstrated the variability that exists in wound closure strip application technique, orientation and intended function.These findings suggest further investigation of wound closure strip usage may be needed to help guide clinical practice.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".