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Record W2944696401 · doi:10.5539/jmbr.v9n1p41

Comparison of Serum Biochemical and Haematological Analyses in Patients with Age-Related Macular Degeneration and Diabetic Retinopathy

2019· article· en· W2944696401 on OpenAlexvenueno aff
Müberra Akdoğan, Yasemin Üstündağ, Mehmet Cem Sabaner, Mustafa Doğan

Bibliographic record

VenueJournal of Molecular Biology Research · 2019
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMacular degenerationDiabetic retinopathyDiabetes mellitusTriglycerideInternal medicineUric acidPost-hoc analysisAlbuminRetinopathyAnalysis of varianceCholesterolGastroenterologyEndocrinologyOphthalmology

Abstract

fetched live from OpenAlex

We evaluated biochemical analysis results with the aim of discovering serum levels that have possible effects and differences on age-related macular degeneration (AMD) and diabetic retinopathy (DRP). A retrospective case-control study was conducted between January 2017 and January 2018 on a total of 114 patients (84 DRP, 30 AMD) and 24 age and sex-matched control individuals. Four groups were created; 52 patients with proliferative DRP (PDR), 32 patients with nonproliferative PDR (non-PDR), 30 patients with wet AMD and 24 control individuals. Serum biochemical (HbA1C, fasting glucose, AST, ALT, C-reactive protein, albumin, total protein, uric acid, triglyceride, HDL, LDL, total cholesterol, Na, K, urine albumin) and complete blood count (CBC) analyses were performed at the time of diagnosis. Descriptive statistics, one-way ANOVA and Kruskal-Wallis tests were performed using SPSS to analyse data (Version 22.0). The mean age of patients was 63.3 years ± 6.4 (49-91year), and that of control individuals was 65.3 years ± 9.3 (50-88 year). Post hoc analysis showed statistically significant differences in HbA1c and fasting glucose levels among PDR-AMD, PDR-control, non-PDR-AMD, and non-PDR-control groups (whole, P

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 score0.094
Threshold uncertainty score0.199

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 head0.050
GPT teacher head0.434
Teacher spread0.384 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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