PROPORTION OF PATIENTS WITH MACULAR HOLE SURGERY WHO WOULD HAVE BEEN FAVORABLE OCRIPLASMIN CANDIDATES
Bibliographic record
Abstract
In Brief Purpose: To identify favorable ocriplasmin candidates from a cohort of idiopathic full thickness macular hole surgery patients. Methods: The records of patients with full thickness macular hole who underwent pars plana vitrectomy surgery between 2011 and 2015 were reviewed. Clinical data collected included patient demographics, pre- and post-operative Snellen visual acuity, optical coherence tomography findings, and lens status. The authors defined “favorable” ocriplasmin candidates as patients with focal vitreomacular traction, no epiretinal membrane, and hole size ≤400 μm. The authors further categorized “optimal” candidates as age ≤65, phakic, no epiretinal membrane, with focal vitreomacular traction, and hole size ≤400 μm. Results: The records of 238 patients were assessed; 30.7% were male while mean age was 68.6 ± 8.3 years. The mean logMAR acuity was 1.2 (Snellen 20/317) preoperatively and 0.90 (Snellen 20/159) postoperatively. Optical coherence tomography findings indicated that 46.5% of the macular holes studied were less than ≤400 μm in size, 14.8% had an epiretinal membrane, and 25.3% had vitreomacular traction. A total of 17.7% of study patients were found to be favorable candidates, whereas 3.8% were optimal ocriplasmin candidates. Conclusion: Only a minority of full thickness macular hole surgical candidates in this cohort would be considered favorable ocriplasmin candidates. The proportion of patients with idiopathic macular hole surgery who would have been favorable ocriplasmin candidates is unknown. This retrospective review of 238 patients found that only 17.7% of these patients were favorable candidates and 3.8% were optimal candidates for ocriplasmin treatment.
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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 | 0.001 |
| 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) | 0.003 | 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".