Diagnoses and Outcomes of Prosthetic Replacement of the Ocular Surface Ecosystem Treatment—A Canadian Experience
Bibliographic record
Abstract
OBJECTIVES: To investigate underlying diagnoses and outcomes of patients undergoing Prosthetic Replacement of the Ocular Surface Ecosystem (PROSE) treatment at the first Canadian PROSE center. METHODS: A retrospective chart review was conducted on patients referred for PROSE treatment and fitted with PROSE devices from 2018 to 2020. Data were collected on diagnoses, presenting symptoms, previous lens modalities attempted, best-corrected visual acuities (BCVAs) pre-PROSE and post-PROSE, daily wear time, and failure rates. Best-corrected visual acuities pre-PROSE and post-PROSE were compared to evaluate visual improvement. RESULTS: In total, 78 patients (126 eyes) were analyzed. The most common diagnoses were keratoconus (n=39 eyes) and postcorneal graft (n=15) in the distorted cornea group, and limbal stem cell deficiency (n=17) and graft versus host disease (n=15) in the ocular surface disease (OSD) group. Most frequent symptoms included blur, photophobia, and pain. Most common lens modalities attempted pre-PROSE were conventional scleral lenses and glasses. The overall mean BCVA improvement was 0.40 logarithm of the minimal angle of resolution (logMAR) (4-lines Snellen) (P<0.0001). Best-corrected visual acuities improvement in the distorted cornea group (0.52 logMAR, 5-lines) was significantly greater than in the OSD group (0.29 logMAR, 3-lines) (P=0.004). CONCLUSIONS: Prosthetic replacement of the ocular surface ecosystem treatment can provide significant visual improvement for patients with distorted corneal surfaces and OSDs who failed other lens modalities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".