Postfasciotomy Classification System for Acute Compartment Syndrome of the Leg
Bibliographic record
Abstract
OBJECTIVE: Acute compartment syndrome (ACS) is a true emergency. Even with urgent fasciotomy, there is often muscle damage and need for further surgery. Although ACS is not uncommon, no validated classification system exists to aid in efficient and clear communication. The aim of this study was to establish and validate a classification system for the consequences of ACS treated with fasciotomy. METHODS: Using a modified Delphi method, an international panel of ACS experts was assembled to establish a grading scheme for the disease and then validate the classification system. The goal was to articulate discrete grades of ACS related to fasciotomy findings and associated costs. A pilot analysis was used to determine questions that were clear to the respondents. Discussion of this analysis resulted in another round of cases used for 24 other raters. The 24 individuals implemented the classification system 2 separate times to compare outcomes for 32 clinical cases. The accuracy and reproducibility of the classification system were subsequently calculated based on the providers' responses. RESULTS: The Fleiss Kappa of all raters was at 0.711, showing a strong agreement between the 24 raters. Secondary validation was performed for paired 276 raters and correlation was tested using the Kendall coefficient. The median correlation coefficient was 0.855. All 276 pairs had statistically significant correlation. Correlation coefficient between the first and second rating sessions was strong with the median pair scoring at 0.867. All surgeons had statistically significant internal consistency. CONCLUSION: This new ACS classification system may be applied to better understand the impact of ACS on patient outcomes and economic costs for leg ACS.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 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) | 0.010 | 0.002 |
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".