Single-capture ultra-widefield guided swept-source optical coherence tomography in the management of rhegmatogenous retinal detachment and associated peripheral vitreoretinal pathology
Bibliographic record
Abstract
BACKGROUND/AIM: To assess the utility of single-capture ultra-widefield confocal scanning laser ophthalmoscope with integrated swept-source optical coherence tomography (UWF-SS-OCT) in the management of rhegmatogenous retinal detachment (RRD) and associated peripheral pathology. METHODS: 138 eyes of 101 consecutive patients with RRD and/or associated peripheral pathology at a vitreoretinal practice in Toronto, Canada between December 2020 and February 2021 that had UWF-SS-OCT with interpretable scans were included. A 200 degree fundus photograph was used to navigate a built-in 100 kHz UWF-SS-OCT to image pathology with a high-density 23 mm line scan and/or volume scan. Main outcomes were the microstructural details of the RRD and/or associated peripheral pathology and longitudinal assessment of response to laser retinopexy and cryopexy with UWF-SS-OCT. RESULTS: 56.5% (78/138) of eyes had prior or current RRD (6 eyes had combined retinoschisis detachment), 31.1% (43/138) had retinal tears/holes and 14.5% (20/138) had lattice degeneration. UWF-SS-OCT impacted management and was useful in determining the presence or absence of vitreoretinal traction with retinal holes or tears. It was also helpful in differentiating retinal detachment, schisis-detachment and retinoschisis in cases where it was not apparent clinically. There were also several novel findings such as vitreous adhesion at the posterior border of a retinal dialysis. UWF-SS-OCT was performed longitudinally before and immediately following laser retinopexy (n=22) and cryopexy (n=4). Microstructural changes were consistent with chorioretinal adhesion immediately following laser versus postprocedure day 6 following cryopexy. CONCLUSION: Single-capture UWF-SS-OCT enabled novel insights in RRD and associated peripheral vitreoretinal pathology.
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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 | 0.002 |
| 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".