Predicting the EQ-5D-5L Utility Scores From the Impact of Vision Impairment Questionnaire in Thai Patients
Bibliographic record
Abstract
Objectives This study aims to predict the EQ-5D-5L utility scores from the impact of vision impairment (IVI) questionnaire in Thai patients using mapping techniques. Methods This is a secondary data analysis. A total of 499 patients with multiple levels of visual impairment were recruited from King Chulalongkorn Memorial Hospital in Thailand between February and July 2022. Ordinary least square, Tobit, censored least absolute deviation, and adjusted limited dependent variable mixture model regression models were used to map the IVI questionnaire onto EQ-5D-5L index scores. IVI domain scores, IVI total score, gender, age, employment status, and best corrected visual acuity were included as predictors. Performance metrics including root mean square error, mean absolute error, and adjusted R 2 were used to determine the best predictive model. Results The results indicated that EQ-5D-5L index scores were significantly associated with the reading and emotional well-being domains of the IVI. Among sociodemographic and clinical variables, higher age score was significantly associated with lower EQ-5D index scores ( P < .01). The mean predicted EQ-5D-5L value (0.803) was lower than the mean observed value (0.808). The adjusted limited dependent variable mixture model 1-component model demonstrated the best predictive performance (root mean square error 0.137, mean absolute error 0.101, adjusted R 2 0.689). Conclusions Mapping techniques effectively predicted EQ-5D-5L utility scores from the IVI questionnaire in Thai patients. The predicted model enhances decision analysis by capturing health utility values, informing quality-adjusted life-years, and supporting health economic evaluations when vision-specific measures are available.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".