Protective effect of soft contact lenses after Boston keratoprosthesis
Bibliographic record
Abstract
PURPOSE: To evaluate associations between preoperative diagnosis, soft contact lens (SCL) retention and complications. METHODS: A retrospective chart review was conducted of 92 adult patients (103 eyes) who received a Boston keratoprosthesis type I at the Massachusetts's Eye and Ear Infirmary or the Flaum Eye Institute. Records were reviewed for preoperative diagnosis, SCL retention and subsequent complications. Preoperative categories included 16 autoimmune (Stevens-Johnson syndrome, ocular cicatricial pemphigoid, rheumatoid arthritis and uveitis), 9 chemical injury and 67 'other' (aniridia, postoperative infection, dystrophies, keratopathies) patients. RESULTS: 50% of the lenses had been lost the first time after about a year. A subset (n=17) experienced more than 2 SCL losses per year; this group is comprised of 1 patient with autoimmune diseases, 2 patients with chemical injuries and 14 patients with 'other' diseases. The preoperative diagnosis was not predictive of contact lens retention. However, multivariate analysis demonstrated that the absence of a contact lens was an independent risk factor for postoperative complications, such as corneal melts with or without aqueous humour leak/extrusion and infections. CONCLUSIONS: Presence of a contact lens after Boston keratoprosthesis implantation decreases the risk of postoperative complications; this has been clinically experienced by ophthalmologists, but never before has the benefit of contact lens use in this patient population been statistically documented.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".