Roller Coasters and Retinal Detachment: Case Series and Review of Acceleration-Deceleration Retinal Injury
Bibliographic record
Abstract
Introduction: Anecdotal reports and limited reports suggest a possible link between activities involving rapid acceleration and retinal detachment. We present two novel such cases and review existing literature to investigate the plausibility of this association and delineate in what populations such an association may be more likely. Case Presentation: We report 2 cases of retinal detachment following roller coaster riding. The first, a 24-year-old woman with a family history of retinal detachment, presented with floaters after consecutive rides and was found to have an inferior temporal macula-sparing retinal detachment with associated retinal breaks. The second case, a 25-year-old female with a history of high myopia, presented with visual field defect and was found to have a macula-on retinal detachment with an accompanying tear at the edge of an area of lattice degeneration. Both were successfully treated with pneumatic retinopexy followed by laser retinopexy. Conclusion: Rapid acceleration/deceleration forces, such as those experienced on roller coasters, could potentially lead to retinal detachment. Structural predisposition is likely necessary for acceleration/deceleration injury to lead to retinal detachment, with all known cases having risk factors, including high myopia and positive family history. These same forces in eyes without structural predisposition have resulted in hemorrhage, but not 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".