Trends in Enrollment, Clinical Characteristics, Treatment, and Outcomes According to Age in Non–ST-Segment–Elevation Acute Coronary Syndromes Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Representation by age ensures appropriate translation of clinical trial results to practice, but, historically, older patients have been underrepresented in clinical trial populations. As the general population has aged, it is unknown whether clinical trial enrollment has changed in parallel. METHODS AND RESULTS: We studied time trends in enrollment, clinical characteristics, treatment, and outcomes by age among 76 141 patients with non-ST-segment-elevation acute coronary syndrome enrolled in 11 phase III clinical trials over 17 years (1994-2010). Overall, 19.7% of patients were ≥75 years; this proportion increased from 16% during 1994 to 1997 to 21% during 1998 to 2001 and 23.2% during 2002 to 2005, but declined to 20.2% in 2006 to 2010. The number of comorbidities increased with successive time periods irrespective of age. There were substantial increases in the use of evidence-based medication in-hospital and at discharge regardless of age. Although predicted 6-month mortality increased slightly over time, observed 6-month mortality declined significantly in all age strata (1994-1997 versus 2006-2010: <65 years: 3.0% versus 1.9%; 65-74 years: 7.5% versus 3.4%; 75-79 years: 13.0% versus 6.5%; 80-84 years: 17.6% versus 8.2%; and ≥85 years: 24.8% versus 12.6%). CONCLUSIONS: The distribution of enrollment by age in phase III non-ST-segment-elevation acute coronary syndrome trials was unchanged over time. Irrespective of age, post-myocardial infarction mortality decreased significantly over time, concurrent with increased evidence-based care and despite increasing comorbidities. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00089895.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".