Digoxin–mortality: randomized vs. observational comparison in the DIG trial
Bibliographic record
Abstract
AIMS: The Digitalis Investigation Group (DIG) trial, the only large randomized trial of digoxin in heart failure, reported a neutral effect on mortality and a significant reduction in heart failure hospitalizations. Recent observational studies reported increased mortality with digoxin treatment. We present further analyses of the DIG trial displaying the inability to control bias in observational treatment comparisons despite extensive statistical adjustments. METHODS AND RESULTS: Forty-four percent of the 6800 patients in the DIG trial had been treated with digoxin before randomization, and half of them were randomly withdrawn from digoxin treatment. We contrast the main randomization-based result of the DIG trial with the observational non-randomized comparison of patients pre-treated or not pre-treated with digoxin. Mortality [hazard ratio (HR) 1.22, 95% confidence interval (CI) 1.12-1.34; P < 0.001] and heart failure hospitalizations (HR 1.47, 95% CI 1.33-1.61; P < 0.001) were significantly higher in patients pre-treated with digoxin even after adjustment for baseline population differences. The higher risks for both outcomes in those who had previously received digoxin persisted even if they received placebo during the trial (HR 1.24, 95% CI 1.10-1.40; P < 0.001). This sharply contradicts the neutral effect on mortality and the significant reduction in heart failure hospitalizations observed in the randomized comparison. CONCLUSION: Prescription of digoxin is an indicator of disease severity and worse prognosis, which cannot be fully accounted for by covariate adjustments in the DIG trial where patients were well-characterized. It is unlikely that weaker research approaches (observational studies of administrative data or registries) can provide more reliable estimates of the effects of cardiac glycosides.
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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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".