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Record W3213005855 · doi:10.1101/2021.11.08.467772

Cerebral perfusion imaging: Hypoxia-induced deoxyhemoglobin or gadolinium?

2021· preprint· en· W3213005855 on OpenAlexaff
Ece Su Sayin, Jacob Schulman, Julien Poublanc, Harrison T. Levine, Lashmi Venkatraghavan, Kâmil Uludaǧ, James Duffin, Joseph A. Fisher, David J. Mikulis, Olivia Sobczyk

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicLanthanide and Transition Metal Complexes
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersInstitute for Basic Science
KeywordsGadoliniumHypoxia (environmental)Magnetic resonance imagingPerfusionPerfusion scanningCerebral perfusion pressureCerebral blood flowCerebral blood volumeMedicineNuclear medicineCardiologyChemistryRadiologyOxygen

Abstract

fetched live from OpenAlex

Abstract Assessment of resting cerebrovascular perfusion measures (mean transit time, cerebral blood flow and cerebral blood volume) with magnetic resonance imaging currently requires the intravascular injection of the dynamic susceptibility contrast agent gadolinium. An initial comparison between hypoxia-induced deoxyhemoglobin and gadolinium was made for these measures in six healthy participants. A bolus of deoxyhemoglobin is generated in the lung via transient hypoxia induced by an available computer-controlled gas blender technology employing sequential gas delivery (RespirAct™). We hypothesised and confirmed perfusion measures from both susceptibility contrast agents would yield similar spatial patterns of cerebrovascular perfusion measures. We conclude that hypoxia-induced deoxyhemoglobin, an endogenously, non-invasively generated, non-allergenic, non-toxic, recyclable, environmentally innocuous molecule, may be suitable to become the first new magnetic resonance imaging susceptibility contrast agent introduction since gadolinium.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.238
Teacher spread0.212 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

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