Influence of Diabetes on the Validity Glaucoma Screening by Frequency Doubling Perimetry: A Hospital-Based Study in Oman
Bibliographic record
Abstract
BACKGROUND: A hospital-based study was conducted in 2007 in Oman to determine the validity of frequency doubling perimetry (FDP). The authors have compared the ability of FDP to detect glaucoma among patients with and without diabetes. METHODS: This was a validity study of the screening procedure. An ophthalmologist and an optometrist examined Omani persons >30 years of age with and without diabetes. They determined glaucomatous field changes by using FDP. The glaucoma specialist labeled the eye and the person as suffering from glaucoma if two criteria were present: (1) optic cup disc and other retinal changes suggestive of glaucoma and (2) field changes on automated perimeter suggestive of glaucoma. Ocular pressure was measured with a Tono-Pen (Medtronic Ophthalmics, Jacksonville, FL). We calculated the validity of glaucoma screening by FDP among persons both with diabetes and without diabetes. RESULTS: We examined 111 eyes of 56 Omani patients (55 eyes of 28 patients with diabetes and 56 eyes of 28 persons without diabetes). Two persons had a history of glaucoma in both eyes. Glaucoma screening by FDP detected five eyes (two eyes of patients with diabetes and three eyes of persons without diabetes) with glaucoma. The specificity of glaucoma screening among those with and without diabetes was 97.4% and 97.3%, respectively. The sensitivity was 6% and 11%, respectively, in these two groups. CONCLUSIONS: Validity of glaucoma screening by FDP is not different among those with and without diabetes. In countries with high rates of diabetes, FDP could be used as a first-level screening tool for glaucoma.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".