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Record W2898888900 · doi:10.1089/neu.2018.6182

Comparison of Performance of Different Optimal Cerebral Perfusion Pressure Parameters for Outcome Prediction in Adult Traumatic Brain Injury: A Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study

2018· article· en· W2898888900 on OpenAlexaff
Frederick A. Zeiler, Ari Ercole, Manuel Cabeleira, Marco Carbonara, Nino Stocchetti, David K. Menon, Peter Smielewski, Marek Czosnyka, Audny Anke, Ronny Beer, Bo‐Michael Bellander, András Büki, Giorgio Chevallard, Arturo Chieregato, Giuseppe Citerio, Endre Czeiter, Bart Depreitere, George A. Eapen, Shirin Frisvold, Raimund Helbok, Stefan Jankowski, Daniel Kondziella, Lars‐Owe Koskinen, Geert Meyfroidt, Kirsten Moeller, David Nelson, Anna Piippo-Karjalainen, Andreea Rădoi, Arminas Ragauskas, Rahul Raj, Jonathan R. Rhodes, Saulius Ročka, Rolf Rossaint, Juan Sahuquillo, Oliver Sakowitz, Ana Stevanovic, Nina Sundström, Riikka Takala, Tomas Tamošuitis, Olli Tenovuo, Peter Vajkoczy, Alessia Vargiolu, Rimantas Vilcinis, Stefan Wolf, Alexander Younsi

Bibliographic record

VenueJournal of Neurotrauma · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of ManitobaManitoba Health
FundersEuropean CommissionDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsTraumatic brain injuryCerebral perfusion pressureMedicineIntracranial pressureReceiver operating characteristicArea under the curveLogistic regressionCorrelationInternal medicineCardiologyAnesthesiaPerfusionMathematics

Abstract

fetched live from OpenAlex

It has been postulated previously that individualized cerebral perfusion pressure (CPP) targets can be derived from cerebrovascular reactivity indices. Differences between real CPP and target CPP (named generically optimal CPP) has been linked to global outcome in adult traumatic brain injury (TBI). Different vascular reactivity indices can be utilized in the determination. The goal of this study is to evaluate CPPopt parameters, derived from three intracranial pressure (ICP)-derived cerebrovascular reactivity indices, and determine which one is superior for 6- to 12-month outcome prediction. Using the prospectively collected data from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study, the following indices of cerebrovascular reactivity were derived: pressure reactivity index (PRx; correlation between ICP and mean arterial pressure [MAP]), pulse amplitude index (PAx; correlation between pulse amplitude of ICP [AMP] and MAP), and RAC (correlation between AMP and CPP). CPPopt was derived using each index. Univariate logistic regression models were created to assess the association between CPPopt with global dichotomized outcome at 6 to 12 months, as assessed by Glasgow Outcome Score-Extended. Models were compared via area under the receiver operating curve (AUC) and Delong's Test. A total of 204 patients had available data. CPPopt derived from PRx, PAx, and RAC performed variably in their association with outcomes. PRx- and RAC-based CPPopt performed similarly, with RAC parameters trending towards highest AUC values. PAx-based CPPopt parameters failed to reach significant associations with dichotomized outcomes at 6 to 12 months. CPPopt parameters derived from PRx and RAC appear similar in their overall ability for 6- to 12-month outcome prediction in moderate/severe adult TBI.

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)0.022
metaresearch head score (Gemma)0.027
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 score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.142
GPT teacher head0.423
Teacher spread0.281 · 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

Citations83
Published2018
Admission routes1
Has abstractyes

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