Impact of tamponade agent on retinal displacement following pars plana vitrectomy for rhegmatogenous retinal detachment repair: a computer simulation model
Bibliographic record
Abstract
Abstract Purpose Rhegmatogenous retinal detachment (RRD) repair with silicone oil (SO) tamponade has been observed to cause less retinal displacement than gas tamponade. Here, we assessed the mechanism and theoretical extent of retinal displacement in SO versus air tamponade in pars plana vitrectomy (PPV). Design Computer simulation model. Methods Tamponade scale and subretinal scale simulation models previously developed were used to assess the physical forces, fluid dynamics and retinal deformations in SO versus air PPV. Results When comparing a SO tamponade and an air tamponade occupying 93% of the ocular cavity, the SO tamponade has a Bond number that is an order of magnitude smaller and has a much lower contact angle (99° versus 125°) and contact pressure (0.0230 versus 1.44 mmHg). With a greater contact area and contact pressure, an air tamponade squeezes subretinal fluid away from contact points, displacing the retina by a maximum length of approximately 700 μm in our model. In contrast, for a SO tamponade, the contact pressure is much lower with a very small magnitude of retinal displacement (~ 50 μm). Conclusions We showed that SO tamponade leads to significantly less retinal displacement than air tamponades. Although we do not recommend the routine use of primary SO because of its several disadvantages compared with gas, our findings can be further utilized to develop novel tamponade agents that minimize the risk of retinal displacement following RRD repair.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".