Effect of Tourniquet Use During Surgical Treatment of Open Fractures
Bibliographic record
Abstract
BACKGROUND: We sought to evaluate whether tourniquet use, with the resultant ischemia and reperfusion, during surgical treatment of an open lower-extremity fracture was associated with an increased risk of complications. METHODS: This is a retrospective cohort study of 1,351 patients who had an open lower-extremity fracture at or distal to the proximal aspect of the tibia and who participated in the FLOW (Fluid Lavage of Open Wounds) trial. The independent variable was intraoperative tourniquet use, and the primary outcome measures were adjudicated unplanned reoperation within 1 year of the injury and adjudicated nonoperative wound complications. RESULTS: Unplanned reoperation and nonoperative wound complications were roughly even between the no-tourniquet (18.7% and 19.1%, respectively) and tourniquet groups (17.8% and 20.8%) (p = 0.78 and p = 0.52). Following matching, as determined by model interactions, tourniquet use was a significant predictor of unplanned reoperation in Gustilo Type-IIIA (odds ratio, 3.60; 95% confidence interval, 1.16 to 11.78) and IIIB fractures (odds ratio, 16.61; 95% confidence interval, 2.15 to 355.40). CONCLUSIONS: The present study showed that tourniquet use did not influence the likelihood of complications following surgical treatment of an open lower-extremity fracture. However, in cases of severe open fractures, tourniquet use was associated with increased odds of unplanned reoperation; surgeons should be cautious with regard to tourniquet use in this setting. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".