Non-invasive monitoring of cyclodialysis cleft using anterior segment optical coherence tomography and its role in informing clinical treatment decisions
Bibliographic record
Abstract
PURPOSE: Anterior segment optical coherence tomography (AS-OCT) is an emerging imaging modality with an expanding role in glaucoma diagnosis and management. We present a series of two cases of iatrogenic cyclodialysis cleft and their conservative management being directly informed by non-invasive AS-OCT monitoring. OBSERVATIONS: Retrospective case series. A 51 year-old male and a 29 year-old male each underwent gonioscopy-assisted transluminal trabeculotomy for uncontrolled glaucoma with a cyclodialysis cleft being diagnosed postoperatively and then monitored using serial AS-OCT images. In both cases, conservative medical management was initially employed. Worsening hypotony maculopathy and decreasing best corrected visual acuity were evident in both cases at times when gonioscopy yielded inadequate visualization to meaningfully inform treatment decisions. Escalation to more invasive therapies was therefore considered. AS-OCT imaging revealed consistent anatomical improvement at each follow-up and ultimately both clefts closed without treatment escalation. CONCLUSIONS AND IMPORTANCE: AS-OCT played a critical role in the diagnosis and directly informed the conservative management of both of these cases. This non-invasive imaging modality may allow for deferral of invasive treatment escalation in some cases of cyclodialysis cleft.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".