Risk Factors and Visual Sequel of Non-Arteritic Ischemic Optic Neuropathy in Saudi Arabia
Bibliographic record
Abstract
A BSTRACT Objective: To provide the demographic data, risk factors, and visual prognosis of patients from a Saudi population diagnosed with non-arteritic anterior ischemic optic neuropathy (NAION). Materials and Methods: A retrospective observational case series of 120 patients (146 eyes) with NAION from the King Khaled Eye Specialist Hospital or King Abdulaziz University Hospital from 1998 to 2015. Patients with other retinal pathology or glaucoma were excluded from the study. Additionally, a subgroup analysis was performed to compare the long-term assessment between diabetic and non-diabetic patients and its effects on NAION. Results: The mean duration of follow-up was 1.7 ± 2.4 years. The mean age of the study population was 55.0 ± 10.1 years. NAION was present in the fellow eye of 26 patients, and the median time for involvement was less than 1 year from the presentation. There was no significant difference in the best-corrected visual acuity between diabetics and non-diabetics at presentation or last visit ( P = 0.868, P = 0.599, respectively). Conclusions: The majority of patients with NAION also had coexisting diabetes mellitus. Diabetes mellitus had no significant effect on NAION during the presentation, follow-up, and last visit.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".