MULTICENTRE PROSPECTIVE COHORT STUDY OF A REAL-TIME DEVICE FOR DIAGNOSING ACUTE COMPARTMENT SYNDROME
Bibliographic record
Abstract
Evaluate the clinical utility and physician satisfaction for a real-time, continuous compartment pressure measurement device as a diagnostic aide for rapid and accurate detection of acute compartment syndrome (ACS). Prospective cohort study performed in three Level 1 trauma centres, in a single Canadian province. An intracompartmental pressure measurement device (MY01) was inserted in patients at high-risk of developing ACS and continuous intracompartmental pressure measurements were monitored and correlated to the clinical signs and symptoms of ACS using visual analog scales (VAS) for pain. Patients were indicated for fasciotomies at the surgeon's discretion using VAS pain scores and pressure measurements. Physician satisfaction surveys were distributed after device use. Fifty patients with injuries at high risk of developing ACS were recruited. Four withdrew consent and four did not meet inclusion criteria. Of the forty-two patients included in the study, eight underwent fasciotomies. Six patients had findings consistent with ACS. Elevated perfusion pressure was a statistically significant positive finding in patients with intraoperative confirmation of ACS (p = 0.002, 95% CI = 9.54 to 33.25), whereas there was no statistically significant difference in the VAS pain scores for patients with and without ACS (p = 0.7454, 95% CI = -3.10 to 2.30). Two attending and forty-two residents in Orthopaedics reported over 90% certainty in its placement and 78% confidence in its function. Real-time, continuous, intra-compartmental measurement devices are useful tools for clinicians diagnosing ACS in high-risk patient groups and more reliable that patient reported pain scores, with high user satisfaction and confidence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".