The effect of personality on measures of quality of life related to vision in glaucoma patients
Bibliographic record
Abstract
AIM: To determine the effect of personality on vision-specific health-related quality of life (HRQoL). METHODS: Based on power calculations, 148 individuals diagnosed as having glaucoma or ocular hypertension, without ocular comorbidity, were selected using criteria that included age over 30, no recent or upcoming surgery, the absence of a diagnosis of clinical depression or any other psychiatric illness. Qualifying participants completed the 25-Item National Eye Institute's Visual Function Questionnaire (VFQ), the Neuroticism, Extraversion and Openness Personality Inventory Revised (NEO PI-R) and the 15-Item Geriatric Depression Scale (GDS-15), and provided information regarding their demographic characteristics and past medical history. Each patient also underwent an ocular examination. Data analysis was conducted to determine the relationship between NEO PI-R personality profiles and VFQ scoring, while controlling for the effects of a range of demographic, psychiatric, past medical and clinical ophthalmic variables. RESULTS: Multivariate analysis indicated that after controlling for a range of covariates, three out of five NEO PI-R personality domains shared statistically significant associations with a variety of VFQ total and subscale score measurements. CONCLUSION: Normal variations in personality characteristics influence how patients report their vision-specific HRQoL.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".