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Record W2765649639 · doi:10.1007/s12028-017-0472-x

ICP Versus Laser Doppler Cerebrovascular Reactivity Indices to Assess Brain Autoregulatory Capacity

2017· article· en· W2765649639 on OpenAlexafffund
Frederick A. Zeiler, Joseph E. Donnelly, Danilo Cardim, David Menon, Peter Smielewski, Marek Czosnyka

Bibliographic record

VenueNeurocritical Care · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of ManitobaManitoba Health
FundersKorea Health Industry Development InstituteCambridge Commonwealth TrustManitoba Medical Service FoundationEuropean CommissionNational Institute for Health and Care ResearchUniversity of Manitoba
KeywordsCorrelationMedicineTranscranial DopplerInternal medicineCerebral blood flowCardiologyCerebral perfusion pressureMean arterial pressureLaser Doppler velocimetryCerebral autoregulationIntracranial pressureAutoregulationPearson product-moment correlation coefficientBlood pressureBlood flowAnesthesiaMathematicsStatisticsHeart rateGeometry

Abstract

fetched live from OpenAlex

BACKGROUND: To explore the relationship between various autoregulatory indices in order to determine which approximate small vessel/microvascular (MV) autoregulatory capacity most accurately. METHODS: Utilizing a retrospective cohort of traumatic brain injury patients (N = 41) with: transcranial Doppler (TCD), intracranial pressure (ICP) and cortical laser Doppler flowmetry (LDF), we calculated various continuous indices of autoregulation and cerebrovascular responsiveness: A. ICP derived [pressure reactivity index (PRx)-correlation between ICP and mean arterial pressure (MAP), PAx-correlation between pulse amplitude of ICP (AMP) and MAP, RAC-correlation between AMP and cerebral perfusion pressure (CPP)], B. TCD derived (Mx-correlation between mean flow velocity (FVm) and CPP, Mx_a-correlation between FVm and MAP, Sx-correlation between systolic flow velocity (FVs) and CPP, Sx_a-correlation between FVs and MAP, Dx-correlation between diastolic flow index (FVd) and CPP, Dx_a-correlation between FVd and MAP], and LDF derived (Lx-correlation between LDF cerebral blood flow [CBF] and CPP, Lx_a-correlation between LDF-CBF and MAP). We assessed the relationship between these indices via Pearson correlation, Friedman test, principal component analysis (PCA), agglomerative hierarchal clustering (AHC), and k-means cluster analysis (KMCA). RESULTS: LDF-based autoregulatory index (Lx) was most associated with TCD-based Mx/Mx_a and Dx/Dx_a across Pearson correlation, PCA, AHC, and KMCA. Lx was only remotely associated with ICP-based indices (PRx, PAx, RAC). TCD-based Sx/Sx_a was more closely associated with ICP-derived PRx, PAx and RAC. This indicates that vascular-derived indices of autoregulatory capacity (i.e., TCD and LDF based) covary, with Sx/Sx_a being the exception, whereas indices of cerebrovascular reactivity derived from pulsatile CBV (i.e., ICP indices) appear to not be closely related to those of vascular origin. CONCLUSIONS: Transcranial Doppler Mx is the most closely associated with LDF-based Lx/Lx_a. Both Sx/Sx-a and the ICP-derived indices appear to be dissociated with LDF-based cerebrovascular reactivity, leaving Mx/Mx-a as a better surrogate for the assessment of cortical small vessel/MV cerebrovascular reactivity. Sx/Sx_a cocluster/covary with ICP-derived indices, as seen in our previous work.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.087
GPT teacher head0.339
Teacher spread0.252 · 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 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

Citations41
Published2017
Admission routes2
Has abstractyes

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