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Record W3193862762 · doi:10.2147/opth.s321371

Combined Pars Plana Vitrectomy and Segmental Scleral Buckle for Rhegmatogenous Retinal Detachment with Inferior Retinal Breaks

2021· article· en· W3193862762 on OpenAlexaff
Parnian Arjmand, Tina Felfeli, Efrem D. Mandelcorn

Bibliographic record

VenueClinical ophthalmology · 2021
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsToronto Western HospitalPublic Health OntarioUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsVitrectomyPars planaScleral buckleMedicineRetinal detachmentOphthalmologyRetinalVisual acuityBuckleProliferative vitreoretinopathySurgery

Abstract

fetched live from OpenAlex

PURPOSE: To describe a variation of the traditional segmental scleral buckle (SB) without an encircling band combined with 23-gauge pars plana vitrectomy (PPV) for the management of rhegmatogenous retinal detachment (RRD) with inferior retinal breaks. PATIENTS AND METHODS: This is a single-center, retrospective, consecutive review of all RRDs with inferior retinal breaks that were treated with PPV and segmental SB without an encircling band between May 2019 and February 2020. RESULTS: A total of 12 eyes of 12 patients were included in the study. All patients had at least 1 inferior retinal break and more than 2 clock hours of retinal detachment. Eight eyes had RRD with macular involvement at presentation. Seven eyes had a persistent RRD following previous pneumatic retinopexy (C3F8). All eyes were treated by PPV combined with a segmental #510 sponge without an encircling band. Surgery anatomical success was 100%. Mean logMAR visual acuity was 1 (SD 0.6; 20/160) and 0.5 (SD 0.4; 20/60) at 3 months and last follow-ups, respectively. No scleral buckle-related complications were noted over the 4.1 (SD 0.8) month follow-up period. CONCLUSION: The combined segmental buckling technique is a safe and effective adjunct to PPV in treatment of inferior RRD.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.051
GPT teacher head0.359
Teacher spread0.308 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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