Iron and cardiac ischemia: a natural, quasi‐random experiment comparing eligible with disqualified blood donors (CME)
Bibliographic record
Abstract
BACKGROUND: The theory that elevated iron stores can induce vascular injury and ischemia remains controversial. We conducted a cohort study of the effect of blood donation on the risk of coronary heart disease (CHD) by taking advantage of the quasi-random exclusion of donors who obtained a falsely reactive test for a transmissible disease (TD) marker. STUDY DESIGN AND METHODS: Whole blood donors who were permanently disqualified because of a false-reactive test between 1990 and 2007 in the province of Quebec were compared to donors who remained eligible, matched for baseline characteristics. The incidence of CHD after entry into the study was determined through hospitalization and death records. We compared eligible and disqualified donors using an "intention-to-treat" framework. RESULTS: Overall, 12,357 donors who were permanently disqualified were followed for 124,123 person-years of observation, plus 50,889 donors who remained eligible (516,823 person-years). On average, donors who remained eligible made 0.36 donation/year during follow-up and had an incidence of hospitalizations or deaths attributable to CHD of 3.60/1000 person-years, compared to 3.52 among permanently disqualified donors (rate ratio, 1.02; 95% confidence interval, 0.92-1.13). CONCLUSION: Donors who remained eligible did not have a lower risk of CHD, compared to donors who were permanently disqualified due to a false-reactive TD marker. Because of the quasi-random nature of false-reactive screening tests, this natural experiment has a level of validity approaching that of a randomized trial evaluating the effect of regular blood donation on CHD risk. These results do not support the iron hypothesis.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".