Declining value of preoperative autologous donation
Bibliographic record
Abstract
BACKGROUND: Preoperative autologous blood donation (PABD) has been shown to decrease allogeneic blood transfusion requirements in major elective surgery. Changes in transfusion practice motivated an examination of blood use from 1993 to 2000 of patients participating in the Héma-Québec PABD program. STUDY DESIGN AND METHODS: Blood donation and transfusion, type of surgery, and demographic characteristics were prospectively entered into a computer database for patients participating in the Héma-Québec PABD program. RESULTS: Autologous donations represented from 0.8 to 2 percent of total blood collections and have declined by 26 percent after peaking in 1995. The mean number of units collected per patient declined, as did the number of units transfused per patient and the utilization rate. For radical prostatectomy, knee replacement surgery, hip replacement surgery, and scoliosis, utilization rates were 72, 60, 83, and 78 percent in 1993 compared with 50, 50, 58, and 58 percent in 2000, respectively. In 2000, 18 percent of patients were receiving a 1-unit autologous transfusion. Depending on the surgical procedure, 85 to 95 percent of patients avoided allogeneic transfusion; this did not change significantly from 1993 to 2000. CONCLUSION: Patients participating in the PABD program successfully avoided allogeneic transfusion in over 85 percent of cases. However, declining utilization rates and frequent 1-unit transfusions demonstrate the decreasing utility of PABD over time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".