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Record W4384038031 · doi:10.4103/jpbs.jpbs_215_23

Risk Factors and Visual Sequel of Non-Arteritic Ischemic Optic Neuropathy in Saudi Arabia

2023· article· en· W4384038031 on OpenAlexaff
Othman Jarallah Al Jarallah, Wael A. Alsakran, Alberto Galvez

Bibliographic record

VenueJournal of Pharmacy And Bioallied Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicIntraoperative Neuromonitoring and Anesthetic Effects
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDiabetes mellitusOptic neuropathyAnterior ischemic optic neuropathyOphthalmologyGlaucomaVisual acuityRetrospective cohort studyPresentation (obstetrics)PopulationDiabetic retinopathyCase seriesOptic nervePediatricsSurgeryPathology

Abstract

fetched live from OpenAlex

A BSTRACT Objective: To provide the demographic data, risk factors, and visual prognosis of patients from a Saudi population diagnosed with non-arteritic anterior ischemic optic neuropathy (NAION). Materials and Methods: A retrospective observational case series of 120 patients (146 eyes) with NAION from the King Khaled Eye Specialist Hospital or King Abdulaziz University Hospital from 1998 to 2015. Patients with other retinal pathology or glaucoma were excluded from the study. Additionally, a subgroup analysis was performed to compare the long-term assessment between diabetic and non-diabetic patients and its effects on NAION. Results: The mean duration of follow-up was 1.7 ± 2.4 years. The mean age of the study population was 55.0 ± 10.1 years. NAION was present in the fellow eye of 26 patients, and the median time for involvement was less than 1 year from the presentation. There was no significant difference in the best-corrected visual acuity between diabetics and non-diabetics at presentation or last visit ( P = 0.868, P = 0.599, respectively). Conclusions: The majority of patients with NAION also had coexisting diabetes mellitus. Diabetes mellitus had no significant effect on NAION during the presentation, follow-up, and last visit.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.351
Teacher spread0.320 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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