Wound care practices following in-office cutaneous surgery among family physicians in Canada
Bibliographic record
Abstract
OBJECTIVE: Family physicians (FPs) in Canada routinely perform in-office cutaneous surgery. There is strong evidence to support a moist wound healing environment, resulting in faster healing times and improved cosmesis. However, the wound care practices of FPs have not been previously studied. We aimed to examine the postoperative wound care practices of FPs after in-office cutaneous surgery. METHOD: An online survey was distributed to Canadian FPs to determine post-surgical wound care practices. The survey examined moist versus dry wound healing and the reasons for these recommendations were explored. Additional wound care practices were also studied. Appropriate statistical analyses were undertaken. RESULTS: A total of 573 (91.5%) FPs completed the survey. Just under half (49.2%) of FPs recommended moist wound healing to their patients, while the remaining respondents (50.8%) recommended dry wound healing. The most endorsed reason for both moist and dry wound care recommendations was prior training (63.1% and 65.3%, respectively). Most physicians (57.2%) recommended the use of a cream or ointment postoperatively. While there appeared to be consensus on recommending sun avoidance after cutaneous surgery (77.7%), additional wound care practices varied, including: the use of dressings; cleansing practices; smoking cessation; reduction in physical activity; photoprotection; water exposure; and scar treatment/cosmetic use. CONCLUSION: Almost half of FPs in Canada responding to the survey did not recommend moist wound healing despite strong evidence to support this practice. We also noted a diverse range of postoperative wound care practices after in-office cutaneous surgery. Therefore, these results highlight a critical need for consistent wound care recommendations following cutaneous surgery for FPs in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".