A cluster‐randomized controlled trial of a blood conservation algorithm in patients undergoing total hip joint arthroplasty
Bibliographic record
Abstract
BACKGROUND: The optimum strategy for reducing allogeneic blood transfusion in patients undergoing total hip joint arthroplasty (THJA) is unknown. STUDY DESIGN AND METHODS: The effectiveness of a comprehensive blood conservation algorithm (BCA) was evaluated by means of a cluster randomization trial. Thirty hospitals performing primary THJA were randomly assigned to implement the algorithm or to continue with usual care (UC). Subsequently, the institutional rate of allogeneic transfusion was determined for 60 consecutive patients who underwent surgery at each site. The BCA consisted of patient and provider education, hemoglobin-based recommendations for specific blood conservation strategies (recombinant human erythropoietin [rHuEPO] or autologous blood donation [ABD]) and transfusion guidelines. The main outcome measure was the institutional allogeneic transfusion rate. RESULTS: One hospital withdrew consent after randomization, resulting in 14 hospitals assigned to BCA and 15 to UC. In the BCA arm, the institutional rates of rHuEPO use and ABD participation were 20.1 and 27.1 percent compared to 0.6 and 25.8 percent, respectively, in the UC arm. The allogeneic transfusion rate was substantially reduced in hospitals assigned to the BCA group (p = 0.02; absolute risk reduction, 9.6% [26.1% UC vs. 16.5% BCA]). Multivariate analysis of patient-level data showed that assignment to the UC arm was an independent risk factor for allogeneic transfusion (p = 0.037; odds ratio, 1.8; 95% confidence interval, 1.0-3.1) when adjusted for other prognostic factors. No differences were observed in the use of autologous blood. CONCLUSION: A comprehensive approach to blood conservation was superior to UC for reducing allogeneic transfusion in patients undergoing THJA.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".