Bibliographic record
Abstract
PURPOSE: To assess the efficacy of collagen cross-linking in the treatment of late-onset bleb leakage. METHODS: A retrospective, interventional case-series. Cross-linking was performed for late-onset bleb leakage after failure of standard medical therapy. Primary outcome is measured by complete resolution of bleb leakage. Secondary outcomes including intraocular pressure and corrected distance visual acuity (CDVA) were documented over 1 year and were analyzed using repeated measure ANOVA. RESULTS: Seven patients underwent cross-linking for bleb leakage between 2012 and 2013. Five (71%) had complete resolution of bleb leakage. Intraocular pressure improved from 3.9±1.0 to 7.4±1.1 mm Hg at 1 to 2 months (P=0.0003) and remained at 9.6±3.0 mm Hg at final visit (P=0.06). The corresponding CDVA also improved from 0.6±0.2 to 0.38±0.37 logMAR (P=0.0069) at 1 to 2 months before stabilizing at 0.38±0.44 (P=0.0021) at 1 year. Two cases (29%) had persistent bleb leakage after treatment. Four patients (57%) had resolved hypotony (≥6 mm Hg) at final visit. No adverse event was reported. CONCLUSIONS: Collagen cross-linking is a noninvasive treatment offering reasonable success rate for filtering bleb leakage, and may spare patients invasive surgical bleb revisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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".