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Record W2791831081 · doi:10.1371/journal.pone.0193592

Structural, functional and blood perfusion changes in the rat retina associated with elevated intraocular pressure, measured simultaneously with a combined OCT+ERG system

2018· article· en· W2791831081 on OpenAlexafffund
Bingyao Tan, Benjamin MacLellan, Erik Mason, Kostadinka Bizheva

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsErgPerfusionRetinaIntraocular pressureOphthalmologyElectroretinographyMedicineAnatomyBiologyCardiologyRetinalNeuroscience

Abstract

fetched live from OpenAlex

Acute elevation of intraocular pressure (IOP) to ischemic and non-ischemic levels can cause temporary or permanent changes in the retinal morphology, function and blood flow/blood perfusion. Previously, such changes in the retina were assessed separately with different methods in clinical studies and animal models. In this study, we used a combined OCT+ ERG system in combination with Doppler OCT and OCT angiography (OCTA) imaging protocols, in order to evaluate simultaneously and correlate changes in the retinal morphology, the retinal functional response to visual stimulation, and the retinal blood flow/blood perfusion, associated with IOP elevation to ischemic and non-ischemic levels in rats. Results from this study suggest that the inner retina responds faster to IOP elevation to levels greater than 30 mmHg with significant reduction of the total retinal blood flow (TRBF), decrease of the capillaries' perfusion and reduction of the ON bipolar cells contribution to the ERG traces. Furthermore, this study showed that ischemic levels of IOP elevation cause an additional significant decrease in the ERG photoreceptor response in the posterior retina. Thirty minutes after IOP normalization, retinal morphology, blood flow and blood perfusion recovered to baseline values, while retinal function did not recover completely.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.474

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 head0.019
GPT teacher head0.194
Teacher spread0.175 · 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
Published2018
Admission routes2
Has abstractyes

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