Factors influencing the adoption of blood alternatives to minimize allogeneic transfusion: the perspective of eight Ontario hospitals.
Bibliographic record
Abstract
OBJECTIVE: To identify and describe the factors influencing the use and nonuse of blood-sparing methods such as preoperative autologous donation, acute normovolemic hemodilution, and the use of cell salvage devices, hemostatic agents and erythropoietin. DESIGN: An interview survey. SETTING: Eight Ontario hospitals. METHOD: Interviews were conducted with chiefs of surgery, orthopedics, cardiac surgery and anesthesia, and with heads of transfusion medicine and pharmacy. Hospitals were selected using the qualitative sampling strategy of maximum variation based on their use of the methods (as reported in a previous mail survey). RESULTS: Use of blood-sparing methods was influenced by diverse factors often operating simultaneously. These included the following: characteristics of the method (e.g., evidence of its effectiveness, ease of use, cost); perceptions and experiences of the potential adopters (experience with the method, perception of the current safety of allogeneic blood, perceived convenience or inconvenience of using the method); aspects of the practice setting (inability to move resources between hospital departments, presence of a local clinical champion); and the external environment (patient and public expectations, funding of the blood system, blood shortages). INTERPRETATION: More rational and evidence-based use of blood-sparing methods could be promoted by the adoption of an interdisciplinary, comprehensive, coordinated approach tailored to each patient's needs.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".