Retinal Displacement: Providing New Insights for Retinal Detachment Surgery
Bibliographic record
Abstract
Purpose . To review the current literature on retinal displacement and provide a discussion of potential risk factors, postoperative outcomes, and future directions. Methods . Two databases, MEDLINE and EMBASE, were mined using a directed search strategy to identify all articles on retinal displacement. Results . We identified 1522 articles. A total of n = 14 articles were retained. We provide an overview on the potential influence of surgical type ( n = 4), tamponade agents ( n = 5), postoperative posture ( n = 6), and preoperative retinal status ( n = 5) on incidence of retinal displacement and visual outcomes ( n = 8). Discussion . Pars plana vitrectomy (PPV) with gas tamponade is associated with displacement rates of up to 72%, typically in a downward direction. Meanwhile, pneumatic retinopexy and PPV with silicone oil may offer similar surgical success with a significantly lower risk of displacement. The impact of heavy liquids such as perfluorocarbon liquid, postoperative positioning and preoperative extent of detachment on displacement remains inconclusive. Patients with displacement had a significantly lower visual acuity and higher rates of distortion than those without displacement. However, not all patients with displacement experienced visual symptoms. Conclusion . Retinal displacement is a new concept in our understanding of retinal detachment. Additional studies are needed to better define its impact on postsurgical outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".