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Record W2607137979 · doi:10.1117/12.2252398

Visually evoked changes in the rat retinal blood flow measured with Doppler optical coherence tomography (Conference Presentation)

2017· article· en· W2607137979 on OpenAlexaff
Bingyao Tan, Erik Mason, Ben MacLellan, Kostadinka Bizheva

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFlickerStimulus (psychology)RetinalRetinaBlood flowOphthalmologyOpticsMedicinePhysicsPsychologyCardiologyComputer science

Abstract

fetched live from OpenAlex

Visually evoked changes of retinal blood flow can serve as an important research tool to investigate eye disease such as glaucoma and diabetic retinopathy. In this study we used a combined, research-grade, high-resolution Doppler OCT+ERG system to study changes in the retinal blood flow (RBF) and retinal neuronal activity in response to visual stimuli of different intensities, durations and type (flicker vs single flash). Specifically, we used white light stimuli of 10 ms and 200 ms single flash, 1s and 2s for flickers stimuli of 20% duty cycle. The study was conducted in-vivo in pigmented rats. Both single flash (SF) and flicker stimuli caused increase in the RBF. The 10 ms SF stimulus did not generate any consistent measurable response, while the 200 ms SF of the same intensity generated ~4% change in the RBF peaking at ~1.5 s after the stimulus onset. Single flash stimuli introduced ~2x smaller change in RBF and ~30% earlier RBF peak response compared to flicker stimuli of the same intensity and duration. Doubling the intensity of SF or flicker stimuli increased the RBF peak magnitude by ~1.5x. Shortening the flicker stimulus duration by 2x increased the RBF recovery rate by 2x, however, had no effect on the rate of RBF change from baseline to peak.

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 score0.060
Threshold uncertainty score0.347

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 head0.039
GPT teacher head0.314
Teacher spread0.275 · 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
Published2017
Admission routes1
Has abstractyes

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