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Record W3163401349 · doi:10.3390/diagnostics11050861

Choroidal Vascularity Map in Unilateral Central Serous Chorioretinopathy: A Comparison with Fellow and Healthy Eyes

2021· article· en· W3163401349 on OpenAlexaff
Niroj Kumar Sahoo, Sumit Randhir Singh, Oliver Beale, Gideon Nkrumah, Mohammed Abdul Rasheed, Asiya Jabeen, Kiran Kumar Vupparaboina, Mohammed Nasar Ibrahim, Filippo Tatti, Khushboo Chandra, Michele Lanza, Claudio Iovino, Enrico Peiretti, Jay Chhablani

Bibliographic record

VenueDiagnostics · 2021
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVascularityMedicineOphthalmologySerous fluidDiabetic retinopathySurgeryDiabetes mellitusPathology

Abstract

fetched live from OpenAlex

Background: To map the choroidal vascularity index and compare two eyes in patients with unilateral central serous chorioretinopathy (CSCR). Methods: This was a retrospective, observational study performed in patients with unilateral CSCR. Choroidal thickness (CT) and Choroidal vascularity index (CVI) were measured and mapped in various zones according to the early treatment diabetic retinopathy (ETDRS) grid. Results: A total of 20 CSCR patients (20 study and 20 fellow eyes) were included in the study. Outer nasal region CT was seen to be significantly lower than central CT (p = 0.042) and inner nasal CT (p = 0.007); outer ring CT was significantly less than central (p = 0.04) and inner ring (p = 0.01) CT in CSCR eyes. On potting all the CVI values against the corresponding CT values, a positive correlation was seen in CSCR eyes (r = 0.54, p < 0.01), which was slightly weaker in fellow eyes (r = 0.3, p < 0.01) and a negative correlation was seen in healthy eyes (r = −0.262, p < 0.01). Conclusions: Correlation between CVI and CT was altered in CSCR eyes as compared to fellow and normal eyes with increasing CVI towards the center of the macula and superiorly in CSCR eyes.

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 score0.003
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.276
Teacher spread0.265 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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