Bibliographic record
Abstract
There are currently numerous techniques described in the literature that attempt to optimize wound closure following a fasciotomy. However, primary closure of fasciotomy wounds continues to be difficult to accomplish successfully because of the underlying edema sustained from the compartment syndrome. The approach described in the present report is simple and physiologically sound, and addresses the underlying pathology. The authors focus on alleviating edema by strictly elevating the limb, followed by primary closure. Twelve consecutive fasciotomy wounds, referred from 2005 to 2012, were closed using this approach. The average wound closure time was 3.4 days (range three to five days) following the initial consultation. All 12 fasciotomy wounds responded with no revisions, complications, failures or loss of skin sensation. The approach was successful in all anatomical locations that were closed and conversion to any techniques currently available in the literature was not necessary. There are no costs associated with this approach, making it practical in settings with limited resources. It has a high success rate, superior cosmetic results and, most importantly, it achieves an efficient closure time. Therefore, this approach is superior to current techniques and should be a part of a plastic surgeon's armamentarium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".