Endoscopic fasciotomy for chronic exertional compartment syndrome of the forearm: Systematic review of outcomes and complications
Bibliographic record
Abstract
BACKGROUND: Endoscopic fasciotomy of the forearm for chronic exertional compartment syndrome (CECS) has gained popularity recently. AIM: To systematically review the literature of endoscopic fasciotomy for CECS of the forearm, aiming to assess the outcomes and complications of the different endoscopic fasciotomy techniques described in the literature. METHODS: On January 18, 2021, PubMed and EMBASE were searched by 3 reviewers independently, and all relevant studies published up to that date were considered based on predetermined inclusion/exclusion criteria. The subject headings "endoscopic fasciotomy" and "compartment syndrome" and their related key terms were used. The Preferred Reporting Item for Systematic Reviews and Meta-Analyses statement was used to screen the articles. RESULTS: A total of seven studies including 183 patients (355 forearms) were included. The mean age of the patients was 31.2 years (range: 15-42 years). The postoperative follow-up duration ranged from 6 wk to 4.9 years. All patients were able to return to sport activities between postoperative weeks 1 to 8. Recurrence of the compartment syndrome occurred in three patients, giving a rate of 1.6% per patient and 0.8% per forearm. The overall complication rate was 8.7% per patient, and 4.5% per forearm. The most common reported complication was hematoma (7 forearms; 2.0%). CONCLUSION: Endoscopic fasciotomy for CECS of the forearm has favorable short- and mid-term outcomes with very low recurrence and complication rates. This, however, needs to be confirmed in larger, long-term follow-up, prospective, comparative studies between open, mini-open and endoscopic fasciotomy techniques.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".