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Record W2969561809 · doi:10.15353/cjo.v81i2.732

Multimodal Imaging in Acute and Chronic Central Serous Chorioretinopathy

2019· article· en· W2969561809 on OpenAlexvenueno aff
An Vo, Jessica Haynes

Bibliographic record

VenueCanadian Journal of Optometry · 2019
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsFluorescein angiographySerous fluidIndocyanine greenMedicineIndocyanine green angiographyOptical coherence tomographyEtiologyAngiographyFundus fluorescein angiographyOptical coherence tomography angiographyOphthalmologyRadiologyPathologyVisual acuity

Abstract

fetched live from OpenAlex

Numerous theories have been proposed regarding the pathophysiology of central serous chorioretinopathy (CSR) with no consensus on its exact etiology. Research has led to a variety of treatment possibilities which target specific mechanisms. Multimodal imaging, including fundus auto-fluorescence (FAF), optic coherence tomography (OCT), fluorescein angiography, and indocyanine green angiography identifies vascular and structural alterations which confirm the diagnosis of CSR and effectively tailors treatment and helps maximize visual prognosis in these patients. Imaging characteristics of CSR and case examples will be explored.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.271
Teacher spread0.267 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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