Limb Occlusion Pressure Versus Standard Pneumatic Tourniquet Pressure in Anterior Cruciate Ligament Surgery: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Tourniquets are frequently used to minimize blood loss. Standard pressures (STPs) are typically higher than minimum limb occlusion pressure (LOP), which can contribute to postoperative pain among other complications. We sought to investigate the effect of STP versus LOP on postoperative pain and opioid medication use after anterior cruciate ligament reconstruction (ACLR). METHODS: Sixty patients (age = 37 ± 15 years) undergoing ACLR were recruited and randomized into STP (275 mm Hg; M = 15/F = 15) or LOP (180 ± 29 mm Hg; M = 15/F = 15) group. A photoplethysmography probe was used to determine appropriate tourniquet pressures for the LOP group. Tourniquet and surgical site pain (Visual Analog Scale scores 0 to 10), as well as opioid medication usage, was recorded for 14 days after surgery. A generalized linear mixed model was used to detect differences in pain and medication use over the 14 days. The type-I error was defined as = 0.05. RESULTS: Tourniquet site pain was less in the LOP group during postoperative days (PODs) 1 to 5 (P < 0.05) and averaged across the two-week postoperative period (P = 0.015). Surgery site pain was less in the LOP group at PODs 9 and 14 (P < 0.05). Reduced opioid medication use was observed in the LOP group at PODs 3, 4, and 7 and averaged across the postoperative window (P < 0.05). CONCLUSION: Individualized LOPs yield decreased postoperative pain and narcotic use compared with STP during ACLR.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".