Technique for Measuring Limb Occlusion Pressure that Facilitates Personalized Tourniquet Systems: A Randomized Trial
Bibliographic record
Abstract
We developed a technique for measuring patient limb occlusion pressure (LOP) through a tourniquet cuff that overcomes many limitations of existing LOP measurement techniques. The purpose of the study is to determine whether the LOP measured by the proposed technique is statistically or clinically different from that measured by the gold standard Doppler ultrasound technique. The study used randomized crossover multicenter trials. 143 pre- and post-surgical patients with a mean age of 54 years (range 17-86 years) were enrolled in the study. Pneumatic cuffs were applied to the non-operative upper and lower limbs and LOP was measured using the proposed technique and the Doppler ultrasound technique. From a total of 252 usable measurements for each technique (134 for upper limbs and 118 for lower limbs), the mean difference in LOP between the two techniques was 1 ± 8 mmHg for the upper limbs, 0 ± 15 mmHg for the lower limbs, and 1 ± 12 mmHg overall. The differences between the proposed technique and the Doppler technique were neither statistically nor clinically significant. The simplicity, effectiveness, and accuracy of the proposed technique should lead to broader clinical usage and acceptance of LOP measurement, thus leading to safer, personalized pressures in surgical tourniquet applications.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".