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Record W2785335263 · doi:10.1089/neu.2017.5595

Intracranial and Extracranial Injury Burden as Drivers of Impaired Cerebrovascular Reactivity in Traumatic Brain Injury

2018· article· en· W2785335263 on OpenAlexaff
Frederick A. Zeiler, Joseph E. Donnelly, Basil Nourallah, Eric Peter Thelin, Leanne Calviello, Peter Smielewski, Marek Czosnyka, Ari Ercole, David K. Menon

Bibliographic record

VenueJournal of Neurotrauma · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Manitoba
FundersSvenska Sällskapet för Medicinsk ForskningSvenska LäkaresällskapetCambridge Commonwealth TrustEuropean CommissionNational Institute for Health and Care Research
KeywordsTraumatic brain injuryMedicineSubarachnoid hemorrhageDiffuse axonal injuryInjury Severity ScoreGlasgow Coma ScaleAutoregulationInternal medicineIntracranial pressureSeverity of illnessAPACHE IICardiologyAnesthesiaPoison controlInjury preventionBlood pressureEmergency medicineIntensive care unitPsychiatry

Abstract

fetched live from OpenAlex

Impaired cerebrovascular reactivity has been associated with outcome following traumatic brain injury (TBI), but it is unknown how it is affected by trauma severity. Thus, we aimed to explore the relationship between intracranial (IC) and extracranial (EC) injury burden and cerebrovascular reactivity in TBI patients. We retrospectively included critically ill TBI patients. IC injury burden included detailed lesion and computerized tomography (CT) scoring (i.e., Marshall, Rotterdam, Helsinki, and Stockholm Scores) on admission. EC injury burden was characterized using the injury severity score (ISS) and the Acute Physiology and Chronic Health Evaluation II (APACHE II) score. Pressure reactivity index (PRx), pulse amplitude index (PAx), and RAC were used to assess autoregulation/cerebrovascular reactivity. We used univariate and multi-variate logistic regression techniques to explore relationships between IC and EC injury burden and autoregulation indices. A total of 358 patients were assessed. ISS and all IC CT scoring systems were poor predictors of impaired cerebrovascular reactivity. Only subdural hematomas and thickness of subarachnoid hemorrhage (SAH; p < 0.05, respectively) were consistently associated with dysfunctional cerebrovascular reactivity. High age (p < 0.01 for all) and admission APACHE II scores (p < 0.05 for all) were the two variables most strongly associated with abnormal cerebrovascular reactivity. In summary, diffuse IC injury markers (thickness of SAH and the presence of a subdural hematoma) and APACHE II were most associated with dysfunction in cerebrovascular reactivity after TBI. Standard CT scoring systems and evidence of macroscopic parenchymal damage are poor predictors, implicating potentially both microscopic injury patterns and host response as drivers of dysfunctional cerebrovascular reactivity. Age remains a major variable associated with cerebrovascular reactivity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.315
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations33
Published2018
Admission routes1
Has abstractyes

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