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Record W2898955799 · doi:10.1097/iae.0000000000002369

SEQUENTIAL PNEUMATIC RETINOPEXIES FOR THE TREATMENT OF PRIMARY INFERIOR RHEGMATOGENOUS RETINAL DETACHMENTS WITH INFERIOR BREAKS

2018· article· en· W2898955799 on OpenAlexaff
Alaa AlAli, Serge Bourgault, Roxane J. Hillier, Rajeev H. Muni, Peter J. Kertes

Bibliographic record

VenueRetina · 2018
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreSt. Michael's HospitalUniversité LavalUniversity of Toronto
Fundersnot available
KeywordsMedicineRetinal detachmentOphthalmologyVisual acuityRetinalSurgery

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate a new approach of sequential pneumatic retinopexies for the management of inferior rhegmatogenous retinal detachments (RD) with inferior breaks. METHODS: Multicenter retrospective consecutive case series of inferior RDs caused by retinal breaks located within the inferior 4 clock hours treated with sequential pneumatic retinopexies, 24 to 48 hours apart. A total of 26 patients with inferior RDs secondary to one or more breaks between the 4 o'clock and 8 o'clock meridians were included from September 2007 to February 2012. RESULTS: The mean follow-up duration was 35.3 weeks. Anatomical success at 8 weeks was achieved in 65.4% of all patients (including those with giant retinal tear and patients with previous RD in the study eye). When excluding patients with giant retinal tear and previous RD in the study eye, the anatomical success rate increased to 70%. Overall, the mean visual acuity improved from 1.00 logMAR (Snellen equivalent 20/200) at baseline to 0.38 logMAR (Snellen equivalent 20/50) at last follow-up. CONCLUSION: Sequential pneumatic retinopexy offers a new viable surgical option for the treatment of RDs secondary to inferior breaks.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.029
GPT teacher head0.287
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2018
Admission routes1
Has abstractyes

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