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Record W2520226834 · doi:10.1111/j.1755-3768.2016.0149

Retinal Oximetry and blood flow

2016· article· en· W2520226834 on OpenAlexaff
Chris Hudson, Kalpana Rose, Susith Kulasekara, Richard Cheng, Bryan M. Wong

Bibliographic record

VenueActa Ophthalmologica · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsRetinalHyperoxiaRetinaBlood flowHypoxia (environmental)Oxygen saturationOxygenOphthalmologyMedicineBiologyChemistryInternal medicineNeuroscience

Abstract

fetched live from OpenAlex

Summary The retina has highest metabolic demand compared to any other tissue in the human body and regulation of the retinal blood flow, blood oxygen saturation (SO 2 ) and thereby oxygen delivery (DO 2 ) are crucial to preserve vision and function. The study reports inner retinal DO 2 and consumption (VO 2 ) during controlled and stable normoxia, hyperoxia and hypoxia in humans. Eleven subjects underwent measurement of total retinal blood flow (TRBF) and retinal blood oxygen saturation (SO 2 ) using prototype methodologies of Doppler Spectral Domain Optical Coherence Tomography and Metabolic Hyperspectral Camera, respectively. TRBF decreased significantly (p = 0.010) from 43.17 μ l/min (+12.7) to 36.23 μ l/min (+4.6) during hyperoxia, conversely, TRBF increased significantly (p < 0.008) to 52.89 μ l/min (+10.9) from baseline during hypoxia. The average inner retinal O 2 delivery during normoxia was 8.48 mlO 2 /100 g/min and inner retinal consumption was 3.64 mlO 2 /100 g/min and these values changed during provocation to maintain a stable DO 2 and VO 2 . Change in TRBF and SO 2 reflect metabolic autoregulatory function of the retinal tissue indicating that retinal blood flow and SO 2 are able to precisely compensate for changes in inspired oxygen.

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 score0.455
Threshold uncertainty score0.973

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)0.0010.000

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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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