Inequity of access to investigation and effect on clinical outcomes: prognostic study of coronary angiography for suspected stable angina pectoris
Bibliographic record
Abstract
OBJECTIVES: To determine whether coronary angiography for suspected stable angina pectoris is underused in older patients, women, south Asian patients, and those from socioeconomically deprived areas, and, if it is, whether this is associated with higher coronary event rates. DESIGN: Multicentre cohort with five year follow-up. SETTING: Six ambulatory care clinics in England. PARTICIPANTS: 1375 consecutive patients in whom coronary angiography was individually rated as appropriate with the Rand consensus method. MAIN OUTCOME MEASURES: Receipt of angiography (420 procedures); coronary mortality and acute coronary syndrome events. RESULTS: In a multivariable analysis, angiography was less likely to be performed in patients aged over 64 compared with those aged under 50 (hazard ratio 0.60, 95% confidence interval 0.38 to 0.96), women compared with men (0.42, 0.35 to 0.50), south Asians compared with white people (0.48, 0.34 to 0.67), and patients in the most deprived fifth compared with the other four fifths (0.66, 0.40 to 1.08). Not undergoing angiography when it was deemed appropriate was associated with higher rates of coronary event. CONCLUSIONS: At an early stage after presentation with suspected angina, coronary angiography is underused in older people, women, south Asians, and people from deprived areas. Not receiving appropriate angiography was associated with a higher risk of coronary events in all groups. Interventions based on clinical guidance that supports individualised management decisions might improve access and outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".