Bibliographic record
Abstract
BACKGROUND/AIMS: Uveitis is a major cause of visual morbidity in the working age group. The authors investigated the duration, degree, and causes of visual loss in uveitis patients with the aim of better defining the visual morbidity and identifying potential risk factors. METHODS: A retrospective, non-interventional, observational survey of 315 consecutive patients attending a tertiary referral uveitis service. RESULTS: The mean duration of follow up was 36.7 months. Reduced vision (< or =6/18) was found in 220/315 (69.95%) of the patients with a subset of 120 patients having vision < or =6/60. Unilateral visual loss occurred in 109 (49.54%), while 111 (50.45%) had bilateral loss. The mean duration of visual loss was 21 months. Of the 148 patients with pan-uveitis, 125 (84.45%) had reduced vision, with 66 (53%) having vision < or =6/60. Main causes of visual loss were cystoid macular oedema (CMO) (59/220, 26.8%), cataract (39/220, 17.7%), and combination of CMO and cataract (44/220, 20%). The following were predictive of a poorer visual prognosis: pan-uveitis (p = 0.0005), bilateral inflammation (p = 0.0005), increasing duration of reduced vision (p = 0.0005), an Indian or Pakistani ethnic background (p = 0.004), and increasing patient age (p = 0.02). CONCLUSION: Prolonged visual loss occurred in two thirds of uveitis patients, with 70 (22%) patients meeting the criteria for legal blindness at some point in their follow up. Older patients with bilateral inflammation and an increasing duration of reduced vision are at the greatest risk of severe visual loss (< or =6/60). CMO and cataract were responsible for visual loss in 64.5% of patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".