Preference-based Glaucoma-specific Health-related Quality of Life Instrument: Development of the Health Utility for Glaucoma
Bibliographic record
Abstract
PURPOSE: To develop a descriptive system for a glaucoma-specific preference-based health-related quality of life (HRQoL) instrument: the Health Utility for Glaucoma (HUG-5). METHODS: The descriptive system was developed in 2 stages: item identification and item selection. A systematic literature review of HRQoL assessment of glaucoma was conducted using a comprehensive search strategy. Purposeful sampling was used to recruit patients with different clinical characteristics. Relevant items were presented to glaucoma patients through face-to-face, semistructured interviews. Framework methodology was applied to analyze interview content. The recurring themes identified through an iterative content analysis represented topics of most importance and relevance to patients. These themes formed the domains of the HUG-5 descriptive system. Three versions of the descriptive system, differing in explanatory detail, were pilot tested using a focus group. RESULTS: The literature review identified 19 articles which contained 266 items. These items were included for the full-text review and were used to develop an interview guide. From 12 patient interviews, 22 themes were identified and grouped into 5 domains that informed the 5 questions of the descriptive system. The HUG-5 measures visual discomfort, mobility, daily life activities, emotional well-being, and social activities. Each question has 5 response levels that range from "no problem" to "severe problem." The focus group comprised 7 additional patients unanimously preferred the version that contained detailed, specific examples to support each question. CONCLUSIONS: A 5-domain descriptive system of a glaucoma-specific preference-based instrument, the HUG-5, was developed and remains to be evaluated for validity and reliability in the glaucoma patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".