Predictive value of local and core laboratory echocardiographic assessment of cardiac function in patients with chronic stable angina: The ACTION study
Bibliographic record
Abstract
AIMS: To evaluate the relationship between echocardiographic cardiac function and outcome in patients with stable symptomatic angina. METHODS: Baseline echo left ventricular ejection fraction and volume data measured in a central laboratory was available for 7016 patients (92% of the total) participating in the ACTION trial (A Coronary disease Trial Investigating Outcome with Nifedipine GITS). Ejection fraction was also measured by investigators. Evaluation of the different echocardiographic variables was based on adjusted hazard ratios comparing the unfavourable limit of the 90% range of the variable concerned to the favourable limit. RESULTS: The centrally measured ejection fraction was the most powerful predictor of all-cause death (adjusted hazard ratio=2.5), myocardial infarction, any stroke or transient ischaemic attack and overt heart failure (adjusted hazard ratio=4.5). The addition of either end systolic volume or end diastolic volume to ejection fraction did not materially affect the power of prediction. Compared to the central ejection fraction measurement, the investigator-measured ejection fraction was a less powerful predictor for all outcomes considered. CONCLUSION: Routine echocardiography carefully analysed by standardised methods provides useful prognostic information in patients with stable angina, including for total mortality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".