Retroprosthetic Membrane Formation in Boston Keratoprosthesis Type 1: Incidence, Predisposing Factors, Complications, and Treatment
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the incidence, rate of formation, and risk factors of retroprosthetic membrane (RPM) after Boston type 1 keratoprosthesis (BKPro) as well as identify secondary complications linked to its formation and describe its treatment. METHODS: This was a retrospective cohort study. One hundred thirty-three eyes of 115 patients who underwent BKPro implantation at the Centre Hospitalier de l'Université de Montréal from 2008 to 2017 were included with at least 1-year follow-up. Charts were reviewed, and data were collected, including incidence of RPM formation, preoperative and intraoperative risk factors, postoperative complications, and treatment modalities. RESULTS: The mean follow-up was 5.5 years, and 39% (n = 52) of eyes developed RPM. No etiologies were found to be significantly associated with RPM development. Simultaneous lensectomy and simultaneous intraocular lens extraction were respectively positively and negatively correlated with RPM development [odds ratio (OR): 0.3590, 95% CI: 0.1629-0.7570, P = 0.0097; OR = 2.646, 95% CI: 1.273-5.585, P = 0.0086, respectively]. As for postoperative complications, the strongest correlation was between RPM and retinal detachment (OR = 6.16, 95% CI: 2.14-16.14; P = 0.0004). RPM development was also positively associated with the development of hypotony and corneal melt. Nd:YAG membranectomy was the most common treatment, performed in 83% of eyes with RPM (n = 24), with 58% resolution. CONCLUSIONS: More than one third of patients who undergo BKPro will develop RPM, requiring close monitoring for retinal detachment. Simultaneous intraocular lens extraction seems protective, whereas simultaneous lens extraction increases the rate of RPM formation significantly. Nd:YAG membranectomy can be used as an initial noninvasive approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".