Optimization of surgical tourniquet usage to improve patient outcomes: Translational cross-disciplinary implications of a surgical practice survey
Bibliographic record
Abstract
Tourniquet use is common practice in many millions of orthopaedic procedures annually. Recent reviews of risks and benefits of surgical tourniquet use have primarily involved meta-analyses, many of which have forgone a comprehensive risk-benefit analysis to simply question whether "tourniquet or no tourniquet" use produces improved patient outcomes, often leading to limited, inconclusive, or conflicting results. To investigate further, a pilot survey was undertaken to determine current practices, opinions, and understandings among orthopaedic surgeons in Canada regarding use of surgical tourniquets in total knee arthroplasties (TKAs). Results of the pilot survey showed a wide range of understanding and practice associated with tourniquet use in TKAs, especially regarding tourniquet pressures and tourniquet times, two key factors known from basic research and clinical studies to impact the safety and efficacy of tourniquet use. The wide variation of use indicated by the survey results reveals important implications for surgeons, researchers, educators, and biomedical engineers, to better understand the association between key tourniquet parameters and outcomes assessed in research, which may be factors leading to their often limited, inconclusive, and conflicting results. Lastly, we provide an overview of the overly simplified assessments of tourniquet use in meta-analyses, whose conclusions may not provide an understanding of how or whether key tourniquet parameters might be optimized to retain the benefits of tourniquet use while mitigating the associated real or perceived risks.
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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 | 0.065 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".