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Record W3159772874 · doi:10.18240/ijo.2021.06.11

Evaluation of retinal and choroidal changes in patients with Alzheimer’s type dementia using optical coherence tomography angiography

2021· article· en· W3159772874 on OpenAlexaboutno aff
Li Jun, Na Li, Huan Yu, Yanlin Wu, Xi Shen

Bibliographic record

VenueInternational Journal of Ophthalmology · 2021
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOphthalmologyRetinalFundus (uterus)PerfusionElectroretinographyChoroidal neovascularizationDiabetic retinopathyCardiology

Abstract

fetched live from OpenAlex

AIM: To evaluate the changes in fundus parameters in patients with Alzheimer's type dementia (ATD) using optical coherence tomography angiography (OCTA), to record flash electroretinograms (ERG) using the RETeval system and to explore changes in retinal function. METHODS: Twenty-nine patients with ATD and 26 age-matched normal subjects were enrolled. All subjects underwent OCTA scans to analyse the superficial retinal vessel parameters in the macular area, including the vessel length density, the vessel perfusion density and the area of foveal avascular zone (FAZ), as well as the choroidal thickness. The differences between the patients with ATD and the normal control group were compared and explored the relevant factors affecting vessel parameters. We also recorded the flash ERGs using the RETeval system and intended to explore changes in retinal function by analysing the ERG image amplitude in patients with ATD. RESULTS: <0.001). These parameters were correlated with the Mini-Mental State Examination (MMSE) score and the Montreal Cognitive Assessment (MoCA). CONCLUSION: Patients with ATD exhibit decreases in the parameters associated with fundus. In addition, these indicators significantly correlate with the MMSE score and the MoCA score. OCTA may be an adjunct tool with strong potential to track changes in the diagnosis and monitoring the progression of the disease.

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 score0.005
Threshold uncertainty score0.313

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 head0.041
GPT teacher head0.333
Teacher spread0.292 · 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

Citations25
Published2021
Admission routes1
Has abstractyes

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