Diagnostic Techniques in Acute Compartment Syndrome of the Leg
Bibliographic record
Abstract
OBJECTIVES: To review the efficacy of the current diagnostic methods of acute compartment syndrome (ACS) after leg fractures. DATA SOURCES: A Medline (PubMed) search of the English literature extending from 1950 to May 2007 was performed using "compartment syndromes" as the main key word. Also a manual search of orthopaedic texts was performed. STUDY SELECTION AND EXTRACTION: The results were limited to articles involving human subjects. Of 2605 primary titles, 489 abstracts limited to compartment syndromes in the leg and 577 articles related to the diagnosis of compartment syndromes were identified and their abstracts reviewed. Further articles were identified by reviewing the references. Sixty-six articles were found to be relevant to diagnostic techniques for compartment syndrome in the leg and formed the basis of this review. CONCLUSIONS: Early diagnosis of an ACS is important. Despite its drawbacks, clinical assessment is still the diagnostic cornerstone of ACS. Intracompartmental pressure measurement can confirm the diagnosis in suspected patients and may have a role in the diagnosis of this condition in unconscious patients or those unable to cooperate. Whitesides suggests that the perfusion of the compartment depends on the difference between the diastolic blood pressure and the intracompartmental pressure. They recommend fasciotomy when this pressure difference, known as the Delta p, is less than 30 mm Hg. Access to a precise, reliable, and noninvasive method for early diagnosis of ACS would be a landmark achievement in orthopaedic and emergency medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".