Time to achieve best postoperative visual acuity following Boston keratoprosthesis surgery
Bibliographic record
Abstract
BACKGROUND/AIMS: To evaluate the time needed for patients with Boston type 1 keratoprosthesis (KPro) to reach their best-corrected visual acuity (BCVA) and all contributing factors. METHODS: We retrospectively reviewed 137 consecutive eyes from 118 patients, measured how long they needed to reach their BCVA and looked at factors that might affect this time duration including patient demographics, ocular comorbidities and postoperative complications. RESULTS: The mean follow-up was 5.49 years. The median time to BCVA postoperatively was 6 months, with 47% of patients achieving their BCVA by 3 months. The mean best achieved logMAR visual acuity was 0.71, representing a gain of 6 lines on the Snellen visual acuity chart. Postoperative glaucoma, retroprosthetic membrane (RPM) and endophthalmitis prolonged this duration. We found no correlation between the following factors and time to BCVA: gender, age, indication for KPro surgery, primary versus secondary KPro, number of previous penetrating keratoplasties, previous retinal surgery, intraoperative anterior vitrectomy and preoperative glaucoma. CONCLUSION: In our retrospective cohort, the majority of subjects reached their BCVA between 3 and 6 months after KPro implantation. This duration was significantly prolonged by the development of postoperative glaucoma, RPM and endophthalmitis.
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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 | 0.004 |
| 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".