Preference-based disease-specific health-related quality of life instrument for glaucoma: a mixed methods study protocol
Bibliographic record
Abstract
INTRODUCTION: A primary objective of healthcare services is to improve patients' health and health-related quality of life (HRQoL). Glaucoma, which affects a substantial proportion of the world population, has a significant detrimental impact on HRQoL. Although there are a number of glaucoma-specific questionnaires to measure HRQoL, none is preference-based which prevent them from being used in health economic evaluation. The proposed study is aimed to develop a preference-based instrument that is capable of capturing important effects specific to glaucoma and treatments on HRQoL and is scored based on the patients' preferences. METHODS: A sequential, exploratory mixed methods design will be used to guide the development and evaluation of the HRQoL instrument. The study consists of several stages to be implemented sequentially: item identification, item selection, validation and valuation. The instrument items will be identified and selected through a literature review and the conduct of a qualitative study. Validation will be conducted to establish psychometric properties of the instrument followed by a valuation exercise to derive utility scores for the health states described. ETHICS AND DISSEMINATION: This study has been approved by the Trillium Health Partners Research Ethics Board (ID number 753). All personal information will be de-identified with the identification code kept in a secured location including the rest of the study data. Only qualified and study-related personnel will be allowed to access the data. The results of the study will be distributed widely through peer-reviewed journals, conferences and internal meetings.
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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 | 0.073 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".