Extended Time to Wound Closure Is Associated With Increased Risk of Heterotopic Ossification of the Elbow
Bibliographic record
Abstract
Heterotopic ossification (HO) is a well-recognized complication of burn injury that can result in significantly compromised limb function. The etiology and optimal treatment strategy for HO remain elusive. The purpose of this study was to examine the relationship between delay in elbow wound closure and the development of HO. We performed a case-control study to examine the relationship between delay in wound closure and development of HO. Cases (HO patients) were identified using our patient registry and matched with patients of similar age, burn size, and sex who did not develop HO. Time to wound closure was compared using bivariate statistics and the odds for developing HO based on time to wound closure was modeled using multivariate logistic regression. During the study period, a total of 45 patients developed elbow HO. When compared with controls matched for age, burn size, and sex, elbow wounds were open significantly longer in the cases than in the controls (48.7 days vs 24.2 days, P < .01). On multivariate logistic regression, the adjusted odds ratio was 1.08 (95% CI 1.04-1.12, P < .01). Time to elbow wound closure significantly impacts the risk of development of heterotopic ossification. Therefore, to minimize risk of HO formation, increased attention is warranted to optimize time to wound closure over joints. In addition, consideration of other soft tissue coverage options such as local flaps, including fascia or muscle flaps, may be warranted in cases of very deep elbow buns with high risk of skin graft failure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".