Tourniquet-induced nerve compression injuries are caused by high pressure levels and gradients – a review of the evidence to guide safe surgical, pre-hospital and blood flow restriction usage
Bibliographic record
Abstract
Tourniquets in orthopaedic surgery safely provide blood free surgical fields, but their use is not without risk. Tourniquets can result in temporary or permanent injury to underlying nerves, muscles, blood vessels and soft tissues. Advances in safety, accuracy and reliability of surgical tourniquet systems have reduced nerve-related injuries by reducing pressure levels and pressure gradients, but that may have resulted in reduced awareness of potential injury mechanisms. Short-term use of pre-hospital tourniquets is effective in preventing life-threatening blood loss, but a better understanding of the differences between tourniquets designed for pre-hospital vs surgical use will provide a framework around which to develop guidelines for admitting to hospital individuals with pre-applied tourniquets. Recent evidence supports the application of tourniquets for blood flow restriction (BFR) therapy to reduce muscular atrophy, increase muscle strength, and stimulate bone growth. BFR therapy when appropriately prescribed can augment a surgeon's treatment plan, improving patient outcomes and reducing recovery time. Key risks, hazards, and mechanisms of injury for surgical, BFR therapy, and pre-hospital tourniquet use are identified, and a description is given of how advances in personalized tourniquet systems have reduced tourniquet-related injuries in these broader settings, increasing patient safety and how these advances are improving treatment outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".