Leading-Digit Bias Influences the Decision to Transfuse Hospitalized Patients
Bibliographic record
Abstract
Leading-digit bias is a heuristic whereby people overemphasize the left-most digit when evaluating numbers (eg 19.99 vs 20). We hypothesized that leading-digit bias influences the interpretation of hemoglobin (Hb) values and therefore red cell transfusion decisions among hospitalized patients. To test our hypothesis, we examined all inpatient red cell transfusions at the University Health Network from January 2016 to September 2021 (n=226,378 units; 4 months missing). We performed a retrospective analysis of pre-transfusion Hb levels compared the frequency of transfusion at 79 vs. 80 g/L in a quadratic regression discontinuity design. Secondary analysis explored other leading-digit thresholds and Benford numbers served as tracer analyses. All analyses performed using R-Studio Version 4.0.2. Our primary analysis included 9,585 red cell units, of which 1,150 were transfused at Hb=79 and 610 at Hb=80. Regression discontinuity analysis showed the threshold accounted for 572 additional red cell units transfused (p=0.006) (Figure 1). The effect was consistent across all subgroups larger than 5000, present in data with and without exclusions, and absent for Benford numbers. Our study is the first to establish leading-digit bias in red cell transfusion. Our findings might be translated into research utilizing quasi-experimental methodology and inform guidelines for transfusion thresholds. Figure 1. Unadjusted regression discontinuity analysis of pre-transfusion hemoglobin (g/L) comparing transfusion frequency above and below bias threshold Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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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 | 0.014 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".