An analysis of a preoperative pediatric autologous blood donation program.
Bibliographic record
Abstract
OBJECTIVE: To determine the efficacy of a pediatric autologous blood donation program. DESIGN: A retrospective study of patient charts and blood-bank records. SETTING: The Children's Hospital of Eastern Ontario, Ottawa, a tertiary care, pediatric centre. PATIENTS: One hundred and seventy-three children who received blood transfusions for a total of 182 procedures between June 1987 and June 1997. INTERVENTIONS: Autologous and homologous blood transfusion required for major surgical intervention, primarily spinal fusion. MAIN OUTCOME MEASURES: Surgeons' accuracy in predicting the number of autologous blood units required for a given procedure, compliance rate (children's ability to donate the requested volume of blood), utilization rate of autologous units and rate of allogeneic transfusion. RESULTS: The surgeons' accuracy in predicting the number of autologous units required for a given procedure was 53.8%. The compliance rate of children to donate the requested amount of blood was 80.3%. In children below the standard age and weight criteria for blood donation the compliance rate was 75.5%. The utilization rate of autologous units obtained was 84.4% and the incidence of allogeneic transfusion was 26.6%. CONCLUSIONS: There was a high rate of compliance and utilization of predonated autologous blood in the children in the study. Preoperative blood donation programs are safe and effective in children, even in those below the standard age and weight criteria of 10 years and 40 kg.
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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 | 0.009 |
| 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 | 0.000 |
| 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".