Rethinking the Paradigm of Using Ps for Diagnosing Compartment Syndrome
Bibliographic record
Abstract
Background: To evaluate the predictive power of 7 clinical signs and symptoms associated with acute compartment syndrome (ACS) of the leg, namely pain, paresthesia, paralysis, pallor, poikilothermia, pulselessness, and pressure on palpation (7P's). Methods: Retrospective data of 357 patients were obtained from the databases of 5-level one trauma centers in Canada, the United States, and France. Inclusion criteria were patients with tibia injuries that received fasciotomies in adults with documented serial clinical assessments. All possible combinations of signs/symptoms used were generated. The combinations were tested for predictive power using 2 machine learning algorithms. Results: Pressure on palpation was the strongest clinical predictor of ACS while pain was the weakest. Using any single P to assess for ACS yields a poor prediction. Increasing the number of Ps improves the performance up to 4Ps, regardless of the composition of the combination. None of the combinations had a perfect predictive power which means that the use of single or multiple Ps does not guarantee diagnosis. Predictive performance indicated that poikilothermia, pallor, and paralysis are not significantly informative. Conclusion: The presence of specific patterns of clinical signs/symptoms associated with ACS seems to influence a surgeon's decision to perform fasciotomy. These data question the gold standard of clinical signs for diagnosis of ACS. The reliance on the Ps classically taught in medical school does not seem to be sufficient for accurate diagnosis. Objective measures such as continuous pressure or a physiologic marker of ischemia may be better indications for compartment syndrome. Level of Evidence: Level III. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.055 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".