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Record W4402478472 · doi:10.1159/000540878

Roller Coasters and Retinal Detachment: Case Series and Review of Acceleration-Deceleration Retinal Injury

2024· article· en· W4402478472 on OpenAlexaff
Lauren Pickel, Miguel Cruz-Pimentel, Sumana Naidu, Robert G. Devenyi, Efrem D. Mandelcorn, Peng Yan

Bibliographic record

VenueCase Reports in Ophthalmology · 2024
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsToronto Western HospitalKensington HealthUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsRetinal detachmentRetinalMedicineOphthalmology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.332
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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