A pre- and post-treatment evaluation of vision-related quality of life in uveitis
Bibliographic record
Abstract
AIM: To study the effect of treatment on vision-related quality of life (VR-QOL) in uveitis patients. MATERIALS AND METHODS: Interviewer-administered questionnaire-based evaluation of visual function and VR-QOL in Tamil-speaking adult patients with active uveitis at presentation and follow-up by the same interviewer. RESULTS: Ninety-eight patients participated in this study. There was a statistically significant improvement in VR-QOL in all the scales following treatment ( P < 0.001). Patients with chronic uveitis showed better improvement upon treatment than patients with acute uveitis. The visual symptoms scale showed moderate gains following treatment (effect size 0.56). Persons with bilateral disease had poorer mean scores compared to those with unilateral disease. Visual acuity was closely correlated with VR-QOL scores. CONCLUSION: The VR-QOL measurement has shown that it is sensitive to demonstrate the problems of patients with uveitis irrespective of their demographic profile. The scores improved significantly in patients with uveitis following treatment and have shown close correlation to visual acuity thus demonstrating that VR-QOL is effective in assessing the response to treatment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".