Hospital volume of throughput and periprocedural and medium-term adverse events after percutaneous coronary intervention: retrospective cohort study of all 17 417 procedures undertaken in Scotland, 1997-2003
Bibliographic record
Abstract
OBJECTIVE: To determine whether percutaneous coronary intervention (PCI) hospital volume of throughput is associated with periprocedural and medium-term events, and whether any associations are independent of differences in case mix. DESIGN: Retrospective cohort study of all PCIs undertaken in Scottish National Health Service hospitals over a six-year period. METHODS: All PCIs in Scotland during 1997-2003 were examined. Linkage to administrative databases identified events over two years' follow up. The risk of events by hospital volume at 30 days and two years was compared by using logistic regression and Cox proportional hazards models. RESULTS: Of the 17,417 PCIs, 4900 (28%) were in low-volume hospitals and 3242 (19%) in high-volume hospitals. After adjustment for case mix, there were no significant differences in risk of death or myocardial infarction. Patients treated in high-volume hospitals were less likely to require emergency surgery (adjusted odds ratio 0.18, 95% confidence interval (CI) 0.07 to 0.54, p = 0.002). Over two years, patients in high-volume hospitals were less likely to undergo surgery (adjusted hazard ratio 0.52, 95% CI 0.35 to 0.75, p = 0.001), but this was offset by an increased likelihood of further PCI. There was no net difference in coronary revascularisation or in overall events. CONCLUSION: Death and myocardial infarction were infrequent complications of PCI and did not differ significantly by volume. Emergency surgery was less common in high-volume hospitals. Over two years, patients treated in high-volume centres were as likely to undergo some form of revascularisation but less likely to undergo surgery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".