BACILLARY LAYER DETACHMENT AND ASSOCIATED ABNORMALITIES IN RHEGMATOGENOUS RETINAL DETACHMENT
Bibliographic record
Abstract
PURPOSE: To describe bacillary layer detachment and related abnormalities of the foveal bouquet in rhegmatogenous retinal detachment and assess their impact on photoreceptor recovery and full-thickness macular hole formation, using optical coherence tomography. METHODS: Prospective cohort of 93 consecutive patients with fovea-off rhegmatogenous retinal detachment presenting to St. Michael's Hospital from January 2020 to April 2022, with gradable preoperative foveal optical coherence tomography. RESULTS: 23.7% (22/93) of patients had evidence of bacillary layer detachment and associated abnormalities. The mean fovea-off duration was 6.4 days (±5.6 SD). 86.4% (19/22) had foveal bacillary layer detachment, 15.8% (3/19) of which had cleavage planes extending from the outer nuclear layer into the myoid zone, and 14% (3/22) had an inner lamellar hole with a residual bridge of photoreceptor remnants (all of which progressed to full-thickness macular hole). Among patients with gradable optical coherence tomography at 3 months post-operatively, 80% (12/15) had ellipsoid zone discontinuity, which persisted in 41% (5/12) at 1 year. CONCLUSION: Bacillary layer detachment was described for the first time in the setting of rhegmatogenous retinal detachment. This is hypothesized to occur from horizontal traction secondary to hydration/lateral expansion of the outer retina in the presence of the Müller cell cone scaffold. Bacillary layer detachment may render the fovea susceptible to further injury, possibly representing a pathophysiological basis for full-thickness macular hole formation in rhegmatogenous retinal detachment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".