Comparison of three heart disease specific health-related quality of life instruments.
Bibliographic record
Abstract
BACKGROUND: We were interested in the feasibility of existing valid specific health-related quality of life [HRQL] instruments, designed for patients with heart failure, angina pectoris, or myocardial infarction [MI], being used to make outcome comparisons among pure or mixed populations of patients with these heart disease diagnoses. METHODS: A battery of specific HRQL questionnaires, including the Minnesota Living with Heart Failure questionnaire, the Seattle Angina Questionnaire, the MacNew Heart Disease questionnaire, the generic SF-36 health status survey, and the Hospital Anxiety and Depression Scale, was mailed to the 205 patients with current mailing addresses and returned by 161 patients [78.5%]. RESULTS: None of the 22 specific and generic HRQL scales differed by diagnostic category. There were significant correlations between all corresponding HRQL scales in the MLHF, SAQ, and MacNew instruments as well as between each of the corresponding specific and generic SF-36 scales. In all cases, the correlations between the specific HRQL scales were numerically greater than those between the specific instruments and the generic SF-36 scales. Patients with and without either anxiety or depression differed significantly on each of the specific HRQL instruments and on the majority of the SF-36 scales. CONCLUSION: The results of this investigation suggest that a common HRQL instrument for patients with heart failure, angina, and MI may prove to be useful when there is an interest in comparing outcomes among pure or mixed populations of patients with heart disease.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".