Survey of Red Blood Cell Transfusion Practices in Medically Ill Patients.
Bibliographic record
Abstract
Abstract Although red cell transfusion is a relatively common treatment used in medically ill patients, much is not known about clinical determinants used to guide its use. A cross-sectional self-administered survey was used to assess the red blood cell transfusion practices of academic medical internists, subspecialty physicians and internal medicine residents who practice on the Medical Teaching Unit (MTU) at the Queen Elizabeth II Health Sciences Center, Halifax, Nova Scotia. We evaluated transfusion thresholds before transfusion and the number of red cell units ordered for four clinical scenarios: chronic obstructive pulmonary disease (COPD), coronary artery disease (CAD), gastrointestinal bleeding and delirium. Clinical characteristics were varied to further clarify clinical determinants of transfusion. The response rate among the forty-nine medical internists and subspecialty physicians was 59%. Among internal medicine residents the response rate was 100% (n=14). The primary area of practice for the majority of respondents was general internal medicine. Most staff physicians attended on the MTU for greater than four weeks. Baseline hemoglobin transfusion thresholds averaged from 75.6 +/− 9.8 g/L in a patient with acute delirium to 91.4 +/− 11.4 g/L in the gastrointestinal bleeding scenario. Range of baseline hemoglobin transfusion thresholds within scenarios was as wide as 60 to 120 g/L. Between the four scenarios, baseline hemoglobin transfusion thresholds differed significantly (p<0.03) except between the CAD and gastrointestinal bleeding scenarios. Clinical factors age and gender did not significantly (p>0.05) alter hemoglobin transfusion thresholds in the CAD and delirium scenarios, but were significant (p<0.02) modifiers of hemoglobin transfusion thresholds along with oxygen saturation, lactic acidosis, hemodynamic stability, requirement for urgent surgery, and chronic surgery in all other scenarios. Comparing staff physicians and internal medicine residents, there was no significant difference between the two groups for baseline hemoglobin transfusion thresholds. In conclusion, among physicians caring for medically ill patients, there is significant variation in red blood cell transfusion practices. Consistent with research in the critical care setting, pre-transfusion hemoglobin, along with other clinical factors, continues to be an important determinant of red cell transfusion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".