The effects of a pneumatic tourniquet on blood loss in total knee arthroplasty.
Bibliographic record
Abstract
OBJECTIVE: In lower-extremity surgery there are significant risks associated with the use of tourniquets. This prospective study was done to assess to what extent these risks may be offset by the potential advantages of tourniquets, namely reductions in blood loss, length of hospital stay and complication rates. DESIGN: A prospective case study. SETTING: A major urban hospital. PATIENTS: Sixty-three consecutive patients scheduled for primary cemented total knee arthroplasty (TKA) were blindly randomized into tourniqet (n = 33) and non-tourniquet (n = 30) groups. INTERVENTION: TKA during which a pneumatic tourniquet was applied or not applied to control blood loss. MAIN OUTCOME MEASURES: Perioperative blood loss, operating time, complication rates, hospital stay and transfusion needs. RESULTS: Differences in the total measured blood loss, intraoperative blood loss and the Hemovac drainage blood loss between the 2 groups were not significantly different (p > 0.25). The calculated total blood loss was actually lower in the non-tourniquet group (p = 0.02). Between the groups there were no statistical differences in surgical time, length of hospital stay, transfusion requirements or rate of complications (although there was a trend to more complications in the tourniquet group (p = 0.06)). CONCLUSION: The effectiveness of a pneumatic tourniquet to control blood loss in TKA is questionable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".