Forearm fasciotomies for acute compartment syndrome: Big data analysis
Bibliographic record
Abstract
ABSTRACT Purpose: To investigate the incidence, risk factors, demographics, and association in the analysis of acute compartment syndrome of the forearm. Methods: A retrospective review of the Trauma Quality Programs data from the American College of Surgeons. This includes 120,556 patients who sustained a forearm fracture from 2015 to 2018 (4 calendar years). The main outcome measurements are fasciotomies performed after sustaining a forearm fracture, thus suggesting acute compartment syndrome. Results: Fasciotomies were performed in 1.6% of all forearm fractures. Open fractures were 5 times more likely to lead to fasciotomies. Being a male was associated with an increased likelihood of fasciotomies of 64%. Complex fractures (OTA type C) exhibited 74% stronger likelihood of fasciotomies compared to simple fractures. Patients with a history of substance abuse disorder (SAD) were 45% more likely to undergo a fasciotomy compared to patient with no SAD. Multiple other factors were addressed while controlling for cofounders. Conclusion: This big data analysis provided a holistic perspective on the risk factors, demographics, and clinical association of ACS in the forearm. There is a clear need for a gold standard diagnosis for ACS to provide better care for the patients: whether it is continuous pressure monitoring, validated biomarkers, or other biomarkers. Level of Evidence: III, retrospective database cohort study. Highlights:
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".