Anterior Segment Eye Assessment of Refractive Surgery Candidates in the Southeast of Iran
Bibliographic record
Abstract
Anterior segment eye parameters are essential factors in diagnosis, screening and management of abnormal ocular conditions. Based on the previous studies, they might differ from one race or population to another. Sistan-and-Baluchestan province, the southeast of Iran, has special weather conditions and race, plus lack of research on these diagnostic factors. Hence, the objective of the present study was to assess anterior segment parameters using pentacam in this area. 800 eyes of subjects which had been referred to the Al-Zahra eye hospital of Zahedan, the capital city of the province, for corneal refractive surgery from October 2014 to March 2015 participated in this research. 95% confidence limits for mean of central corneal thickness, anterior chamber depth and volume were (536.02, 541.20), (3.13, 3.18) and (187.63, 192.58) respectively. Multiple linear regression models showed a lower mean central corneal thickness, and maximum/minimum of keratometry, for males than females, adjusting for age and spherical equivalent. Inversely, anterior chamber depth, and volume were more in males. In order to diagnosis and treating ocular diseases which have effect on retinal thickness, precisely specification of predictive factors is highly needed.
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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 | 0.000 | 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".