Early results using a dynamic method for delayed primary closure of fasciotomy wounds
Bibliographic record
Abstract
Fasciotomy incisions, which are usually performed for compartment syndrome, cannot be closed primarily because of excessive tension across the wound secondary to postischemic swelling of the extremity. Split-thickness skin grafting, the conventional method of fasciotomy closure, is effective but it results in an insensate and cosmetically unappealing wound and is associated with donor site morbidity. Skin has several unique and useful properties that allow for delayed primary closure of wounds despite large tissue defects or significant retraction. These biomechanical properties, which include inherent extensibility and mechanical and biological creep, have been exploited by a variety of techniques for delayed primary closure of fasciotomy wounds. The vessel loop shoelace technique, use of the Sure-Closure skin-stretching device (Comesa), use of a prepositioned cutaneous suture, and several other techniques have shown reasonable wound closure rates and wound cosmesis, but have been criticized because they are expensive, cumbersome to apply and to tighten, or are associated with increased compartment pressures and skin edge necrosis. The following case series presents our results using a new method of dynamic wound closure with a novel device (Canica Design, Inc) applied to six fasciotomy incisions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".