Supplementary Material for: Quality of Life Measures in Aortic Stenosis Research: A Narrative Review
Bibliographic record
Abstract
Background: Elderly patients with aortic stenosis (AS) not only have a reduced life expectancy but also a reduced quality of life (QoL). The benefits of an AS intervention may be considered a balance between a good QoL and a reasonably extended life. However, the different questionnaires being used to determine the QoL, were generally not developed for the specific situation of patients with AS and come with strengths and considerable weaknesses. The objective of this manuscript was to provide an overview of the available QoL instruments in AS research, describe their strengths and weaknesses, and to provide our assessment of the utility of the available scoring instruments for QoL measurements in AS. Summary: We identified and reviewed the following instruments that are used in AS research: Short Form Health Survey (SF-36/SF-12), EuroQol-5D (EQ-5D), the Illness Intrusiveness Rating Scale (IIRS), the HeartQoL, the Kansas City Cardiomyopathy Questionnaire (KCCQ), the Minnesota Living with Heart Failure Questionnaire (MLHF), the MacNew Questionnaire, and the Toronto Aortic Stenosis Quality of Life Questionnaire (TASQ). Key messages: There is no standardized assessment of QoL in patients with AS. Many different questionaires are being used, but they are rarely specific for AS. There is a need for AS specific research into the QoL of patients as life prolongation may compete for an improved QoL in this elderly patient group.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.164 | 0.015 |
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".