Survival analysis for success of Molteno tube implants
Bibliographic record
Abstract
AIM: To apply survival analysis in assessing the long term outcome of Molteno tube implantation and to identify risk factors for failure. METHODS: A retrospective, 10 year, consecutive case series study of 119 eyes that underwent implantation of a Molteno tube. The main outcome measures considered were intraocular pressure (IOP), visual acuity, and complications. RESULTS: A 30% or greater reduction in IOP was achieved in 68.9% of cases. However, the overall, "complete success" rate (IOP <22 mm Hg with no medications) after a mean (SD) follow up period of 43 (33) months (range 6-120) was only 33.6% despite a fall in mean (SD) IOP from 38.2 (8.2) mm Hg to 20.1 (11.0) mm Hg. The "qualified success" rate (IOP <22 mm Hg with or without medications) was 60.5%. Failure was most common in the first postoperative year but could occur after several years, the survival curve having an exponential shape. The only statistically significant risk factor for failure identified was pseudophakia, although eyes with neovascular glaucoma tended to fare poorly. Postoperative IOP tended to be lower after double plate than after single plate implantation. There was no significant difference in outcome based on age, sex, race, previous penetrating keratoplasty, or previous conjunctival surgery. CONCLUSIONS: In eyes at high risk of trabeculectomy failure, implantation of an aqueous shunt device should be considered. Pseudophakia should be considered an additional risk factor for failure. Early failure appeared relatively more common but long term follow up of all cases is recommended to ensure adequate management of late failures.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".