OPTICAL COHERENCE TOMOGRAPHY–BASED POSITIONING REGIMEN FOR MACULAR HOLE SURGERY
Bibliographic record
Abstract
In Brief Purpose: To evaluate an optical coherence tomography (OCT)–based positioning regimen for patients undergoing macular hole surgery. Method: We reviewed the medical records of all patients in our practice who underwent macular hole repair, instituting a modified OCT-based positioning regimen from November 1, 2011 through July 31, 2013. The regimen consisted of prone positioning at the conclusion of surgery with daily OCT imaging until the hole was confirmed closed at which point positioning was halted. Clinical data that were collected and recorded included visual acuities, stage of hole, size of hole, chronicity, preoperative and postoperative OCT imaging, and length of follow-up. Results: We identified 33 patients (35 eyes) with a mean baseline visual acuity of 20/220, a mean hole size of 465 μm. The mean final (postoperative) visual acuity was 20/135 with a mean follow-up of 7.7 months. Six patients (17%) in our study were diagnosed with myopic degeneration. Thirteen patients (37%) were found to have chronic (≥12 months) holes, and 19 (54%) were found to have large holes (>400 μm). Overall, 28 eyes (80%) had persistent closure of macular holes with an OCT-based positioning regimen. In the absence of high risk factors, such as myopic degeneration, chronic or large holes, the closure rate was 92%. In the presence of 2 or 3 of these risk factors, the closure rate was 85% and 74%, respectively. Conclusion: The presence of 2 or 3 high risk factors, such as myopic degeneration, chronic holes (≥12 months), or large holes (>400 μm) can compromise outcomes resulting in reopening after apparent early closure. Based on the presence of these risk factors, a modified postoperative positioning regimen can be used to obtain complete and persistent closure. The presence of 2 or 3 high risk factors, such as myopic degeneration, chronic holes (≥12 months), or large holes (>400 μm) can compromise outcomes resulting in reopening after apparent early closure. Based on the presence of these risk factors, a modified postoperative positioning regimen can be used to obtain complete and persistent closure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".