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Record W4386914044 · doi:10.1177/0271678x231203025

Penumbral cooling in ischemic stroke with intraarterial, intravenous or active conductive head cooling: A thermal modeling study

2023· article· en· W4386914044 on OpenAlexaff
William K. Diprose, A. Muralikrishna Rao, Kaustubha Ghate, Zoe Dyer, Doug Campbell, Mohammed Almekhlafi, P. Alan Barber

Bibliographic record

VenueJournal of Cerebral Blood Flow & Metabolism · 2023
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPenumbraHypothermiaMedicineStroke (engine)IschemiaBrain ischemiaAnesthesiaInternal medicinePhysics

Abstract

fetched live from OpenAlex

brain temperature measurement remains challenging, the effect of different methods of therapeutic hypothermia on penumbral temperature are unknown. We used the COMSOL Multiphysics® software to model a range of cases of therapeutic hypothermia in ischemic stroke. Four ischemic stroke models were developed with ischemic core and/or penumbra volumes between 33-300 mL. Four experiments were performed on each model, including no cooling, and intraarterial, intravenous, and active conductive head cooling. The steady-state temperature of the non-ischemic brain, ischemic penumbra, and ischemic core without cooling was 37.3 °C, 37.5-37.8 °C, and 38.9-39.4 °C respectively. Intraarterial, intravenous and active conductive head cooling reduced non-ischemic brain temperature by 4.3 °C, 2.1 °C, and 0.7-0.8 °C respectively. Intraarterial, intravenous and head cooling reduced the temperature of the ischemic penumbra by 3.9-4.3 °C, 1.9-2.1 °C, and 1.2-3.4 °C respectively. Active conductive head cooling was the only method to selectively reduce penumbral temperature. Clinical studies that measure brain temperature in ischemic stroke patients undergoing therapeutic hypothermia are required to validate these hypothesis-generating findings.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.301
Teacher spread0.268 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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