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Record W2780464109 · doi:10.1089/neu.2017.5364

Transcranial Doppler Systolic Flow Index and ICP-Derived Cerebrovascular Reactivity Indices in Traumatic Brain Injury

2017· article· en· W2780464109 on OpenAlexaff
Frederick A. Zeiler, Danilo Cardim, Joseph E. Donnelly, David K. Menon, Marek Czosnyka, Peter Smielewski

Bibliographic record

VenueJournal of Neurotrauma · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Manitoba
FundersNational Institute for Health and Care Research
KeywordsTranscranial DopplerCorrelationCerebral perfusion pressureCardiologyTraumatic brain injuryMean arterial pressureMedicineIntracranial pressureInternal medicineCerebral blood flowAnesthesiaBlood pressureMathematicsHeart rate

Abstract

fetched live from OpenAlex

The purpose of our study was to explore relationships between transcranial Doppler (TCD) indices of cerebrovascular reactivity and those derived from intracranial pressure (ICP). Goals included: A) confirming previously described co-variance patterns of TCD/ICP indices, and B) describing thresholds for systolic flow index (Sx; correlation between systolic flow velocity [FVs] and cerebral perfusion pressure [CPP]) associated with outcome. In a retrospective cohort of traumatic brain injury (TBI) patients: with TCD and ICP monitoring, we calculated various continuous indices of cerebrovascular reactivity: A) ICP (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 CPP) and B) TCD (mean flow index [Mx]: correlation between mean flow velocity [FVm] and CPP; Mx_a: correlation between FVm and MAP; Sx: correlation between FVs and CPP; Sx_a: correlation between FVs and MAP; Dx: correlation between diastolic flow velocity [FVd] and CPP; Dx_a: correlation between FVd and MAP). We assessed the relationships via various statistical techniques, including: principal component analysis, agglomerative hierarchal clustering, and k-means cluster analysis (KMCA). We performed sequential χ2 testing to define thresholds associated with outcome for Sx/Sx_a. Outcome was assessed at 6 months via dichotomized Glasgow Outcome Score (GOS): A) Favorable (GOS 4 or 5) versus Unfavorable (GOS 3 or less), B) Alive versus Dead. We analyzed 410 recordings in 347 patients. All analyses confirmed our previously described co-variance of Sx/Sx_a with ICP-derived indices. Sx displayed thresholds of −0.15 for unfavorable outcome (p < 0.0001) and −0.20 for mortality (p < 0.0001). Sx_a displayed thresholds of +0.05 (p = 0.019) and −0.10 (p = 0.0001) for alive/dead and favorable/unfavorable outcomes. TCD systolic indices are most closely associated with ICP indices. Sx and Sx_a likely provide better approximation of ICP indices, compared with Mx/Mx_a/Dx/Dx_a. Sx provides superior outcome prediction, versus Mx, with defined thresholds.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.057
GPT teacher head0.321
Teacher spread0.264 · 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

Citations62
Published2017
Admission routes1
Has abstractyes

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