Automated Cuff Occlusion Pressure Effect on Quality of Operative Fields in Foot and Ankle Surgery: A Randomized Prospective Study
Bibliographic record
Abstract
BACKGROUND: Limb occlusion pressure, which is present when blood flow ceases, has not had a practical method described for attainment. An automated tourniquet system was modified to set tourniquet pressure based on measurement of limb occlusion pressure (LOP). In this single surgeon randomized prospective study, the effectiveness of this system was assessed on patients undergoing foot and ankle surgery. MATERIALS AND METHODS: Two hundred forty-four patients were randomized to the study group of automated pressure (n = 112) or to the control group (n = 132). The primary outcome measure was tourniquet pressure used for either group. Secondary measures included the time to set the pressure and number of patients failing LOP measurement. The tourniquet field was assessed intraoperatively and postoperatively in a blinded manner. RESULTS: The tourniquet pressure was significantly lower in the study group at 198.5 ± 20.2 mmHg compared to 259.6 ± 4.4 in the control group (p < 0.001). The time to measure the LOP was 20 ± 6 seconds. Six patients failed to be measured. The quality of the surgical field was judged to be better in the study group based on all three methods of assessment. CONCLUSION: LOP measurement was a practical way of setting tourniquet pressures for limb surgery. The automated pressure averages were lower than those routinely used by most surgeons for thigh tourniquets.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".