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Record W986921002 · doi:10.1001/archopht.124.3.397

Confocal scanning laser Doppler flowmetry in the rat retina: origin of flow signals and dependence on scan depth.

2006· article· en· W986921002 on OpenAlexaff
Balwantray C. Chauhan, Paula K. Yu, Stephen J. Cringle, Dao‐Yi Yu

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLaser Doppler velocimetryBlood flowVenuleRetinaArterioleBalayageConfocalAnatomyBiomedical engineeringCapillary actionMicrocirculationMaterials scienceRetinalDoppler effectMedicineOpticsLaserOphthalmologyPhysicsRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the origin of signals from scanning laser Doppler flowmetry (SLDF) and the influence of axial scan depth on the measurement of blood flow in the rat retina. METHODS: We performed SLDF in 5 adult Sprague-Dawley rats using a specially modified Heidelberg retina flowmeter. Axial scans were obtained from +2 diopters (D) to -3 D (in steps of 0.25 D) or from +1 D to -2 D (in steps of 0.125 D) relative to the retinal surface. Fluorescein isothiocyanate-dextran angiograms were obtained in whole-mounted retinas to visualize the angioarchitecture and identify measurement locations in the SLDF flow maps. Axial SLDF flow profiles were obtained in an artery, vein, arteriole, venule, and capillary bed using the mean blood flow values in 2 x 2-, 4 x 4-, and 10 x 10-pixel measurement windows. RESULTS: The SLDF images showed good correspondence with the angiograms and resolution to third-order arterioles and venules; however, neither the superficial nor deep capillary circulations were visualized. Flow was imaged from large choroidal vessels. Measured flow from capillaries was independent of depth and indistinguishable from background levels. CONCLUSION: The technique of SLDF images blood flow in larger retinal vessels but not in capillaries. Clinical Relevance Scanning laser Doppler flowmetry may not reliably measure capillary blood flow.

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 score0.650
Threshold uncertainty score0.378

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 head0.017
GPT teacher head0.219
Teacher spread0.202 · 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
Published2006
Admission routes1
Has abstractyes

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