Gender differences in descriptions of angina symptoms and health problems immediately prior to angiography: the ACRE study. Appropriateness of Coronary Revascularisation study
Bibliographic record
Abstract
Although the prevalence of angina in women is increasing, women are less likely than men to undergo invasive management of coronary disease. Gender differences in language use may contribute to disparities in management, since the diagnosis: of angina relies on a patient's description of their symptoms. This: study set out to investigate whether gender differences exist in the language used vc when describing angina symptoms and perceived health problems at the time of angiography, which might influence the rate of subsequent revascularisation. Content analysis was used to analyse written accounts of 'symptoms and health problems' in 200 (96 female) patients randomly selected within age strata who were undergoing coronary angiography for chronic stable angina in the Appropriateness of Coronary Revascularisation (ACRE) study. Written free text was coded into seven categories: pain location (chest or arm and throat, neck or jaw); pain character; breathlessness; other symptoms: effects on lifestyle; symptom attributions; and patient discourses ('story' or 'factual'). Women described mure throat, neck or jaw pain than men among those with low physical functioning (p = 0.06), in the presence of coronary artery disease (p = 0.03) and in those who were not subsequently revascularised (p = 0.05). Women also gave more accounts than men of breathlessness and other symptoms, but there was little evidence for gender differences in the use of 'factual' discourses. We conclude that from the time of angiography. gender differences in language use do exist and description of angina pain may influence subsequent revascularisation. Further research is necessary to investigate the nature and consequences of gender differences in language use at this and earlier stages in the referral process. (C) 2001 Elsevier Science Ltd. All rights reserved
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".