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Record W2795091525 · doi:10.1364/brain.2018.bf2c.4

Quantitative Tissue Spectroscopy Techniques for Measuring Cerebral Perfusion and Metabolism

2018· article· en· W2795091525 on OpenAlexaff
Mamadou Diop

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsCerebral blood flowOxygenSpectroscopyCytochrome c oxidaseNear-infrared spectroscopyFunctional near-infrared spectroscopyIn vivo magnetic resonance spectroscopyBiomedical engineeringOxygenationPerfusionChemistryComputer scienceNeuroscienceMedicineInternal medicineBiologyBiochemistryMagnetic resonance imagingPhysics

Abstract

fetched live from OpenAlex

The brain constitutes 2% of the total body weight but accounts for 20% and 25% of total body basal oxygen and glucose consumption, respectively. As well, the brain has very limited energy storage; thus, it relies on adequate blood flow for oxygen and glucose delivery, and disruption in supply can has devastating effects on the brain. In this talk we’ll present three point-of-care optical sensing techniques that can quantify cerebral perfusion and oxygen metabolism (i.e., cerebral metabolic rate of oxygen and oxygenation state of cytochrome c oxidase). We will describe the technologies (time-resolved spectroscopy, diffuse correlation spectroscopy, and hyperspectral continuous-wave near-infrared spectroscopy), the advanced algorithms we developed to analyze the optical data to convert them into physiological parameters, and some applications in animal models of brain injury and neonatal studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.358
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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