Complications associated with Boston keratoprosthesis type 1 and glaucoma drainage devices
Bibliographic record
Abstract
BACKGROUND/AIMS: To compare the complications leading to best-corrected visual acuity (BCVA) loss in patients with Boston keratoprosthesis type 1 (KPro) and glaucoma drainage device (GDD) and those with KPro alone. METHODS: Retrospective case series of all patients who underwent KPro surgery at the Centre Hospitalier de l'Université de Montréal between 2008 and 2011. Preoperative diagnoses, BCVA and complications were tabulated and analysed. RESULTS: KPro surgery was performed in 96 eyes: 18 eyes (19%) had KPro and GDD while 78 eyes (81%) had KPro only. Median BCVA at postoperative 6 months was 20/150 in both groups. Seven eyes (39%) with KPro and GDD experienced vision loss due to complications such as glaucoma progression (three eyes, 22%), tube occlusion (four eyes, 22%) and choroidal haemorrhage (three eyes, 17%). Vitreous incarceration was the most common cause of tube occlusion. Vitreoretinal, glaucoma and infectious complications caused BCVA loss in 16 eyes (21%) with KPro alone (p=0.13). CONCLUSIONS: Glaucoma progression is a major cause of visual decline post-KPro. However, GDD implantation should only be performed in carefully selected patients. Because of a high risk of vitreous incarceration within the tube, a complete pars plana vitrectomy should be performed prior to GDD implantation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".