Impact of Public Reporting of 30-day Mortality on Timing of Death after Coronary Artery Bypass Graft Surgery
Bibliographic record
Abstract
BACKGROUND: Recent reports have raised concerns that public reporting of 30-day mortality after cardiac surgery may delay decisions to withdraw life-sustaining therapies for some patients. The authors sought to examine whether timing of mortality after coronary artery bypass graft surgery significantly increases after day 30 in Massachusetts, a state that reports 30-day mortality. The authors used New York as a comparator state, which reports combined 30-day and all in-hospital mortality, irrespective of time since surgery. METHODS: The authors conducted a retrospective cohort study of patients who underwent coronary artery bypass graft surgery in hospitals in Massachusetts and New York between 2008 and 2013. The authors calculated the empiric daily hazard of in-hospital death without censoring on hospital discharge, and they used joinpoint regression to identify significant changes in the daily hazard over time. RESULTS: In Massachusetts and New York, 24,864 and 63,323 patients underwent coronary artery bypass graft surgery, respectively. In-hospital mortality was low, with 524 deaths (2.1%) in Massachusetts and 1,398 (2.2%) in New York. Joinpoint regression did not identify a change in the daily hazard of in-hospital death at day 30 or 31 in either state; significant joinpoints were identified on day 10 (95% CI, 7 to 15) for Massachusetts and days 2 (95% CI, 2 to 3) and 12 (95% CI, 8 to 15) for New York. CONCLUSIONS: In Massachusetts, a state with a long history of publicly reporting cardiac surgery outcomes at day 30, the authors found no evidence of increased mortality occurring immediately after day 30 for patients who underwent coronary artery bypass graft surgery. These findings suggest that delays in withdrawal of life-sustaining therapy do not routinely occur as an unintended consequence of this type of public reporting.
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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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".