Macular displacement following rhegmatogenous retinal detachment repair
Bibliographic record
Abstract
AIMS: To investigate the incidence of macula displacement and symptoms of distortion following rhegmatogenous retinal detachment (RRD) repair, quantify the displacement where seen and further characterise the nature of the displacement. METHODS: Consecutive patients undergoing primary RRD repair were assessed postoperatively with fundus autofluorescence and optical coherence tomography imaging, and the extent of macula displacement quantified using a novel means. Findings were examined for correlations with symptoms and pre-operative features. RESULTS: Macula displacement was evident postoperatively in 72% of 32 consecutive fovea-involving detachments treated with vitrectomy and gas. It was also evident in 5/17 foveal-sparing cases treated with vitrectomy and gas and in two of two patients with fovea-involving detachments treated with vitrectomy and oil. There was a significant correlation between the presence of macula displacement and symptoms of distortion in the early postoperative period (p=0.013). Symptomatic patients described bending of lines with or without objects appearing smaller or narrower in the operated eye. Quantifying the displacement demonstrated that the extent of displacement was associated with distance from the optic disc (p=0.005) and the extent of retinal detachment. CONCLUSIONS: Displacement of the macula is common following RRD repair and heterogeneous in nature. Most affected patients are symptomatic in the early postoperative period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".