Diagnosis Accuracy for Compartment Syndrome: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether published studies support basing the diagnosis of compartment syndrome of the lower leg on clinical findings, intracompartmental pressure (ICP) monitoring, or both. DATA SOURCES: A PubMed/MEDLINE, Web of Science, and Embase search of the English literature from 1966 to February 2022 was performed. This used "lower extremity" or "leg" or "tibia" and "compartment syndrome" and "pressure" as the subjects. A manual search of the bibliographies was performed and cross-referenced with those used to formulate the American Academy of Orthopaedic Surgeons clinical practice guidelines. STUDY SELECTION AND EXTRACTION: Inclusion criteria were traumatic tibia injuries, presence of data to calculate the sensitivity, specificity, positive and negative predictive values of clinical findings and/or pressure monitoring, and the presence or absence of compartment syndrome as the outcome. A total of 2906 full articles were found, of which 63 were deemed relevant for a detailed review. Seven studies met all eligibility criteria. DATA SYNTHESIS: The likelihood ratio form of Bayes theorem was used to assess the discriminatory ability of the clinical findings and ICP monitoring as tests for compartment syndrome. The predictive value for diagnosing acute compartment syndrome was 21% and 29% for the clinical signs and ICP, respectively. When combining both, the probability reached 68%. CONCLUSIONS: The use of ICP monitoring may be helpful when combined with a clinical assessment to increase the sensitivity and specificity of the overall diagnosis. Previously accepted individual inference values should be revisited with new prospective studies to further characterize the statistical value of each clinical finding. LEVEL OF EVIDENCE: Diagnostic 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 | 0.033 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".