Impact of initial topical medical therapy on short-term quality of life in newly diagnosed patients with primary glaucoma
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of initial topical medical therapy on newly diagnosed glaucoma patients using the Indian Vision Function Questionnaire (IND-VFQ33). PATIENTS AND METHODS: The IND-VFQ33 was used to evaluate the quality of life (QoL) in 62 newly diagnosed patients with moderate to severe primary glaucoma and 60 healthy controls. IND-VFQ33 is a 33 item QoL assessment tool with three domains: General functioning, psychosocial impact and visual symptoms. The glaucoma patients were started on medical therapy and the QoL assessment was repeated after 3 months. RESULTS: Glaucoma patients (mean age: 55.6 ± 9.6 years, range 40-77 years) and controls (mean age: 54.9 ± 6.7 years, 42-73 years) were matched with respect to age (P = 0.72), gender (P = 0.91) and literacy (P = 0.18). Glaucoma patients had significantly worse QoL as compared to controls at baseline across all the three domains (P < 0.001). 3 months after initiation of treatment, the overall QoL life significantly worsened from baseline with a decrease in general functioning (P < 0.001) and psychosocial impact (P = 0.041). Visual acuity in better eye significantly co-related to poor QoL at baseline (P < 0.001) and at 3 months (P = 0.04). In addition, the use of >2 topical medications significantly co-related to poor QoL at 3 months (P = 0.01). CONCLUSIONS: Evaluation using the IND-VFQ33 revealed that newly diagnosed glaucoma patients have a significant worsening of QoL after initiation of topical ocular hypotensive therapy. This should be an important consideration when educating patients about the disease and its therapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".