Are there age and sex differences in the investigation and treatment of heart failure? A population-based study.
Bibliographic record
Abstract
BACKGROUND: Heart failure is a serious, common, and growing problem. Hospital admissions, which account for the bulk of health service costs associated with heart failure, are becoming more frequent. AIM: To determine whether management of heart failure differs by age and sex. METHOD: A retrospective case note review of prevalent cases in 16 general practices in West London. Five hundred and eighty-three patients (57% women) with a diagnosis of heart failure were reviewed. RESULTS: Mean age of patients with heart failure was 78 years (SD = 9.5)--74 years at diagnosis (SD = 10)--and was higher for women than men (76 years versus 71 years, P < 0.001). In 32% of patients there was no record of a chest X-ray, electrocardiogram, or echocardiogram to support diagnosis. Echocardiography, performed in 34% of patients, was less likely in older patients in both sexes (test for trend P = 0.04 in women and 0.02 in men) and, overall, in women (29% compared with 40% of men, P = 0.006). Angiotensin-converting enzyme (ACE) inhibitor treatment, recorded in 54% of patients, decreased with age in both sexes (P < 0.001) and, on unadjusted data, was more likely in men than in women (61% compared with 49%, P = 0.005). On adjustment for age, sex differences in the use of echocardiography and ACE inhibitors were reduced and no longer significant. CONCLUSIONS: With increasing age, men and women with heart failure were less likely to have undergone echocardiography or to have received an ACE inhibitor. When account was taken of age, there were no statistically significant sex differences in management; however, because of the demographic distribution of heart failure, women are disproportionately affected by age differences in management. Clinical trials, physician practice, and service developments in heart failure have neglected older people. This balance should be redressed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".