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Record W2940480312 · doi:10.1101/619361

Brain BOLD MRI O <sub>2</sub> and CO <sub>2</sub> stress testing: Implications for perioperative neurocognitive disorder following surgery

2019· preprint· en· W2940480312 on OpenAlexaff
W. Alan C. Mutch, Renée El‐Gabalawy, Lawrence Ryner, Josep Puig, Marco Essig, Kayla Kilborn, Kelsi Fidler, M. Ruth Graham

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHypocapniaAnesthesiaCerebral blood flowPerioperativeNeurocognitiveMedicineHyperventilationPsychologyCognitionPsychiatryHypercapniaAcidosis

Abstract

fetched live from OpenAlex

Abstract Respiratory end-tidal (ET) gas control is fundamental to anaesthetic management. The range of ET O 2 and CO 2 during the conduct of anaesthesia can significantly deviate from values in the awake state. Recent work shows ET CO 2 influences the incidence of perioperative neurocognitive disorder (POND). We examine the effects of controlled alterations in both ET O 2 and CO 2 on cerebral blood flow (CBF) in awake adults using BOLD MRI. Twelve healthy adults had BOLD and CBF responses measured to alterations in ET CO 2 and O 2 in various combinations commonly observed under anaesthesia. Dynamic alterations in regional BOLD and CBF were seen in all subjects with expected and inverse responses to both stimuli. These effects were incremental and rapid (within seconds). The most dramatic effects were seen with combined hyperoxia and hypocapnia. Inverse responses increased with age. Here we show that human brain CBF responds dramatically to alterations in ET respiratory gas tensions commonly seen during anaesthesia. Such alterations may impact the observed incidence of POND following surgery and intensive care, and is an important area for further investigation.

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)0.007
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.041
GPT teacher head0.275
Teacher spread0.234 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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