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Record W2050507851 · doi:10.4103/2320-3846.147027

Spectral domain optical coherence tomography used to view and quantify choroidal vascular congestion in new subretinal fluid following encircling scleral buckling

2014· article· en· W2050507851 on OpenAlexaff
Mohamed Haji, FlavioA Rezende, Robert Gizicki

Bibliographic record

VenueSaudi Surgical Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsUniversity of TorontoUniversité de Montréal
Fundersnot available
KeywordsIndocyanine greenMedicineLaser Doppler velocimetryChoroidBlood flowOptical coherence tomographyOphthalmologyScleral bucklingRetinal pigment epitheliumRetinalRetinal detachmentRetinaSurgeryRadiologyOptics

Abstract

fetched live from OpenAlex

Known complications of scleral buckling surgery for primary rhegmatogenous retinal detachment include choroidal detachment, vortex vein compression and anterior segment ischemia. Post-operative persistent subretinal fluid following scleral buckling surgery is thought to occur in as many as 55% of cases. Likely mechanisms include impaired retinal pigment epithelium (RPE) function, altered subretinal fluid composition, choroidal ischemia, incomplete drainage of subretinal fluid and altered choroidal blood flow. Indocyanine green (ICG) angiography, laser Doppler velocimetry and scanning laser Doppler flowmetry have showed altered choroidal blood flow, pulsatile ocular blood flow and choroidal venous drainage after scleral buckling.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.282
Teacher spread0.260 · 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.

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
Published2014
Admission routes1
Has abstractyes

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