Red blood cell transfusion practice in elective orthopedic surgery: a multicenter cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The indications for red blood cell (RBC) transfusions remain unclear despite published guidelines. Our hypothesis was that the transfusion practice varies inside the Centre hospitalier de l'Université de Montréal (CHUM). STUDY DESIGN AND METHODS: A total of 701 charts of patients who underwent a knee or hip arthroplasty or prosthesis revision in three hospitals of the CHUM were reviewed. Demography, hemoglobin (Hb) concentrations, details on transfusions, and postoperative adverse events (AEs) were collected up until discharge. The primary outcome was the presence or absence of RBC transfusion. Secondary outcomes were the nadir Hb, number of units transfused, discharge Hb, blood losses, and postoperative AEs. RESULTS: The rate of postoperative transfusion was 29%. We found no significant difference between odds ratios of each site for sex, coronary artery disease, chronic heart failure, type of procedure, American Society of Anesthesiologists physical status, weight, height, body mass index, body surface area, and estimated blood volume. Overall, patients were transfused at a Hb between 75 and 80g/L. Eighty-five percent of postoperative transfusions could be predicted using only nadir Hb and adding patient characteristics did not substantially improve the model (86.1%). Discharge Hb was below 100g/L in 66% of patients. CONCLUSIONS: There was no difference among hospitals regarding the way RBC transfusions are used. Our data suggest that physicians mainly based their decision to transfuse on a single variable, the Hb concentration, with the use of a restrictive strategy. Future trials should focus on the optimal transfusion trigger to adopt in major orthopedic surgery.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it