Transfusion Probability as a Novel Measure for Lab-Guided Medical Decision-Making
Bibliographic record
Abstract
ABSTRACT The clinical decision to transfuse is strongly influenced by laboratory results. Analysis of transfusion decision-making based on pre-transfusion laboratory results (e.g. pre-transfusion hemoglobin) is a common yet misleading approach to study lab-guided transfusion practice. We introduce “Transfusion Probability” as a novel method which overcomes many limitations of pre-transfusion lab result analyses. Under this approach, we estimate the probability of transfusion after results at a specific value (e.g. hemoglobin 7.4 g/dL) or in a range of values (e.g. 7.0-7.9 g/dL) using the proportion of tests followed by transfusion. We provide statistical methodology for causal inference on the effect of patient conditions and apply our method to a large multi-center dataset. Analyses using pre-transfusion and transfusion probability were compared using data from a large longitudinal cohort of hospitalized patients (N=525,032 patients). We found red blood cell transfusion probabilities of 76.2% in the 6.0-6.9 g/dL, 18.9% in the 7.0-7.9 g/dL, and 4.5% in the 8.0-8.9 g/dL hemoglobin range. After confounder adjustment, patients with gastrointestinal bleeding patients were more likely to be transfused across all ranges, with risk differences ranging from 6.6% in the 8.0-8.9 g/dL range to 13.8% in the 6.0-6.9 g/dL range. Pre-transfusion hemoglobin results showed minimal differences between gastrointestinal bleeding patients and other patients in unadjusted (0.00 g/dL) and adjusted analyses (-0.20 g/dL). In contrast to pre-transfusion result analysis, transfusion probability offers a nuanced account of transfusion practice and allows for natural comparisons between patient groups. Wider adoption of transfusion probability analysis may provide direct and actionable insights for clinical decision-making. KEY POINTS Pre-transfusion lab results are a widely used method for studying lab-guided transfusion but subject to many limitations Transfusion probability analysis is a novel and superior approach
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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 | 0.023 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".