Response shift after coronary revascularization
Bibliographic record
Abstract
PURPOSE: The aims of this study were to investigate (1) the extent to which response shift occurs among patients with coronary artery disease (CAD) after coronary revascularization, (2) whether the assessment of changes in health-related quality of life (HRQoL), controlled for response shift, yield more valid estimates of changes in HRQoL, as indicated by stronger associations with criterion measures of change, than without controlling for response shift, and (3) if occurrences of response shift are related to patient characteristics. METHODS: Patients with CAD completed the SF-36 and the Seattle Angina Questionnaire (SAQ7) at baseline and 3 months after coronary revascularization. Sociodemographic, clinical and psychosocial variables were measured with the patient version of the New York Heart Association-class, Subjective Significance Questionnaire, Reconstruction of Life Events Questionnaire (RE-LIFE), and HEXACO personality inventory. Oort's Structural Equation Modeling (SEM) approach was used to investigate response shift. RESULTS: 191 patient completed questionnaires at baseline and at 3 months after treatment. The SF-36 showed recalibration and reprioritization response shift and the SAQ7 reconceptualization response shift. Controlling for these response shift effects did not result in more valid estimates of change. One significant association was found between reprioritization response shift and complete integration of having CAD into their life story, as indicated by the RE-LIFE. CONCLUSION: Results indicate response shift in HRQoL following coronary revascularization. While we did not find an impact of response shift on the estimates of change, the SEM approach provides a more comprehensive insight into the different types of change in HRQoL following coronary revascularization.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".