Case report: Topical pilocarpine ameliorated the accommodation loss and pupillary dilation after micropulse transscleral laser treatment
Bibliographic record
Abstract
BACKGROUND: The present study elucidates a common significant postoperative complication of micropulse transscleral laser treatment (mTLT) and explores its potential management strategies for younger patients with good central vision. CASE PRESENTATION: Three younger Chinese glaucoma patients with good central vision maintained high intraocular pressures (IOPs) (36, 25, and 30 mmHg) on maximally tolerated topical anti-glaucoma medications. All patients were treated with mTLT because of a higher risk of complications with filtering surgery. After the procedure, their best-corrected visual acuities were not significantly changed, IOPs were significantly decreased, and the number of topical anti-glaucoma medicines was gradually decreased. However, all patients complained about reduced near visual acuity (NVA) for 1-5 months. Slit-lamp examination revealed pupillary dilation, and binocular accommodative function examination indicated accommodation loss. After treatment with 2% topical pilocarpine, all patients reported an improvement in NVA. Among them, we could observe pupillary constriction, recovery of accommodation function, and improved NVA, even discontinuation of pilocarpine in Patient 2. CONCLUSION: In younger patients with good central vision, topical pilocarpine might ameliorate accommodation loss and pupillary dilation after mTLT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".