Predictors of Red Cell Transfusion in Medically III Patients.
Bibliographic record
Abstract
Abstract Although red cell transfusion is a relatively common treatment in medically ill patients, much is not known about the clinical determinants used to guide its use. We conducted a retrospective, case-controlled chart review of admissions to the medical teaching units (MTU) at this center (Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia). Forty-two patients who received red blood cell transfusion during admission to the MTU from January to March 31, 2004. Clinical data collected included: age, gender, indication for MTU admission, co-morbid disease, smoking, admission hemoglobin, pre- and post-transfusion hemoglobin, arterial pressure of oxygen, lactic acidosis, parameters of shock, presence of active bleeding, and need for surgery. Logistic regression analysis was used to identify significant clinical determinants of transfusion. Red blood cell transfusion rate was 8%. Of admission diagnoses and co-morbidities, only cancer was associated with a trend for transfusion (p=0.07). Those who received red blood cell transfusion had significantly lower admission hemoglobin levels (p=0.005), higher serum creatinine (p=0.02), and lower mean arterial pressure (p=0.01). Significant predictive clinical factors of red blood cell transfusion included congestive heart failure (OR=3.72, CI 1.034, 27.160), admission hemoglobin level (d/L, OR=0.956, CI 0.927, 0.986), mean arterial pressure (mm Hg, OR=0.942, p=0.005) and serum creatinine (ml/min, OR=1.008, CI 1.003, 1.014). In conclusion, predictors of red blood cell transfusion for medically ill patients include low admission hemoglobin, renal insufficiency, and low blood pressure.
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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 | 0.000 | 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 | 0.001 |
| 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 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".