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Record W4408122471 · doi:10.1097/iae.0000000000004439

Pneumatic Retinopexy Rescue with In-Office Suprachoroidal Viscopexy

2025· article· en· W4408122471 on OpenAlexaffabout
Rajeev H. Muni, Aurora Pecaku, Sue Ellen Demian, Miguel Cruz-Pimentel, Isabela Martins Melo

Bibliographic record

VenueRetina · 2025
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsKensington HealthUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineOphthalmology

Abstract

fetched live from OpenAlex

PURPOSE: To describe in-office suprachoroidal viscopexy (SCVEXY) as a novel adjunct surgical technique with pneumatic retinopexy (PnR) for rhegmatogenous retinal detachment (RRD). METHODS: A 61-year-old pseudophakic man who was failing PnR for a macula-involving RRD underwent rescue SCVEXY at St. Michael's Hospital, Unity Health Toronto, Toronto, Canada. RESULTS: An injection of suprachoroidal sodium hyaluronate 2.3% (Healon 5, Johnson & Johnson Vision) was performed at five o'clock under the causative retinal tear using a 30 G needle with a custom-made guard that exposed 1 mm of the needle. Following the procedure, a dome-shaped suprachoroidal convexity was present in the inferotemporal quadrant. The patient achieved complete reattachment over 2 days with continued positioning. Laser retinopexy was applied around the causative tear, and the viscoelastic reabsorbed over a period of approximately 2 weeks. The retina remained attached until the final follow-up at 9 months. CONCLUSION: Suprachoroidal viscopexy (SCVEXY) is a minimally invasive in-office procedure that creates a temporary suprachoroidal buckle that can be used to rescue failing PnR in RRD. It can be particularly useful to close inferior tears, avoiding the need for operating room procedures such as pars plana vitrectomy or scleral buckle. However, there are still limited data on ideal case selection, efficacy, adverse events, and failure rates.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.264
Teacher spread0.257 · 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 designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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