Anterior Segment Changes After Pilocarpine and Laser Iridotomy for Primary Angle-Closure Suspects With Scheimpflug Photography
Bibliographic record
Abstract
PURPOSE: To assess changes in Scheimpflug-based measurements of the anterior segment after pilocarpine administration and prophylactic laser peripheral iridotomy in narrow anterior chamber angles. METHODS: Thirty-seven eyes in 37 patients with occludable angles were included in this prospective interventional case series. Primary angle-closure suspects (iridotrabecular contact in 3 quadrants or more) were enrolled. Patient evaluation included indentation gonioscopy, Goldmann tonometry, and optic nerve examination. The mean of 3 consecutive Pentacam measurements was taken at baseline, 45 minutes after 2% pilocarpine administration, and 1 month after laser peripheral iridotomy (LPI). Anterior chamber angle (ACA), anterior chamber volume (ACV), anterior chamber depth (ACD), pupil diameter, central corneal thickness, and intraocular pressure were measured. RESULTS: ACV increased significantly after LPI (from a mean ± standard error of 94.6 ± 3.6 mm(3) to 108.8 ± 3.4 mm(3), P<0.001), as did the ACA (26.7 ± 0.9 degrees to 28.2 ± 0.8 degrees, P<0.001). Central corneal thickness decreased significantly after LPI (558.1 ± 5.3 μm to 552.6 ± 5.7 μm, P=0.018). Central ACD increased slightly after LPI, but this was not statistically significant (2.13 ± 0.05 mm to 2.15 ± 0.05 mm, P=0.109). Pupil diameter and intraocular pressure also did not change significantly after LPI. After pilocarpine, the ACV decreased significantly (by 4.3 ± 1.3 mm(3), P=0.009), as did the central ACD (by 0.1 ±0.02 mm, P<0.001) and the pupil diameter (by 0.74 ± 0.06 mm, P<0.001). CONCLUSIONS: Scheimpflug photography demonstrates significant anterior segment changes after pilocarpine and after LPI in primary angle-closure suspects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".