Assessing Outcomes in Individuals Undergoing Fasciotomy for Chronic Exertional Compartment Syndrome of the Leg
Bibliographic record
Abstract
PURPOSE: The purposes of this study were to evaluate patient-reported outcomes after fasciotomy of the leg for chronic exertional compartment syndrome (CECS) and to determine the rate at which revision surgery was required and the prognostic value of intracompartmental pressure (ICP) testing. METHODS: This was a retrospective consecutive case series of patients with CECS who underwent preoperative ICP testing and surgical fasciotomy for treatment of CECS of the leg between September 2001 and January 2012. RESULTS: Of 69 eligible patients, 46 were evaluated at a mean follow-up time of 54.9 months (range, 3.9 to 127.3 months). Forty-two patients met the Pedowitz criteria for CECS diagnosis. Mean score on the Lower Extremity Functional Scale (LEFS) was 70.4 (standard deviation [SD] ± 11.2) at follow-up and 72.3 (SD ± 11.2) at the patient-perceived time of best outcome. Best outcome was reported at a mean time of 14.3 months (range, 0.5 to 84 months). Five of 46 (11%) patients required a revision fasciotomy. Thirty-six of 46 (78%) patients reported being either satisfied (n = 14) or very satisfied (n = 22) at follow-up. The Pedowitz criteria were highly sensitive (97%) but not specific (10%) and had a positive predictive value (PPV) of 79%. CONCLUSIONS: Functional outcomes after fasciotomy for CECS were favorable. ICP testing was shown to be sensitive but not specific. Revision surgery was required for 5 of the 46 patients (11%). Patient satisfaction rates, return to sport, return to preoperative activity levels, and LEFS scores were all high. This case series confirms that fasciotomy is a safe and effective surgical treatment for CECS. LEVEL OF EVIDENCE: Level IV, therapeutic case series.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".