Postfasciotomy Classification System for Acute Compartment Syndrome of the Leg
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it