Understanding the mechanism of retinal displacement following rhegmatogenous retinal detachment repair: A computer simulation model
Bibliographic record
Abstract
Abstract Purpose Retinal displacement is common following rhegmatogenous retinal detachment (RRD) repair. A computer simulation was developed to assess forces applied by a gas tamponade of various sizes in the setting of pneumatic retinopexy (PnR) versus pars plana vitrectomy (PPV). Design Computer simulation model. Methods The contact angle and pressure between the tamponade and the retina were calculated using interfacial tension and the densities of gas and vitreous. A simulation determined the dynamics of fluid motion in the subretinal space and calculated deformations of the retina. Results Bulk flow of fluid away from the tamponade in a direction along gravity stretched the retina and caused displacement in the simulations. Extent of displacement is attributable to the subretinal fluid layer thickness, and area of contact and contact pressure applied by the tamponade. Larger gas tamponades have greater contact pressure applied to the retina. Reducing gas bubble size from 93% to 6.25% with PPV versus PnR, there was a 79% reduction in the mean contact pressure (1.4 mmHg–0.29 mmHg), and a 93% reduction in the surface area of contact (11 cm2–0.8 cm2). Therefore, the contact force applied to the entire retina decreases by 97% from 83 mN (PPV) to 2.9 mN (PnR). The model resembling PnR had more than three times less displacement compared to PPV. Conclusions This model provides a framework to study retinal displacement. Our findings suggest that proportional to their size, gas tamponades stretched the retina by displacing subretinal fluid 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".