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Record W4205277287 · doi:10.1097/mcc.0000000000000920

Intracranial pressure management: moving beyond guidelines

2022· article· en· W4205277287 on OpenAlexaff

Bibliographic record

VenueCurrent Opinion in Critical Care · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIntracranial pressurePsychological interventionClinical PracticeMEDLINEClass (philosophy)Disease management

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this study was to provide an overview on advances in intracranial pressure (ICP) protocols for care, moving from traditional to more recent concepts. RECENT FINDINGS: Deep understanding of mechanics and dynamics of fluids and solids have been introduced for intracranial physiology. The amplitude or the harmonics of the cerebral-spinal fluid and the cerebral blood waves shows more information about ICP than just a numeric threshold. When the ICP overcome the compensatory mechanisms that maintain the compliance within the skull, an intracranial compartment syndrome (ICCS) is defined. Autoregulation monitoring emerge as critical tool to recognize CPP management. Measurement of brain tissue oxygen will be a critical intervention for diagnosing an ICCS. Surgical procedures focused on increasing the physiological compliance and increasing the volume of the compartments of the skull. SUMMARY: ICP management is a complex task, moving far than numeric thresholds for activation of interventions. The interactions of intracranial elements requires new interpretations moving beyond classical theories. Most of the traditional clinical studies supporting ICP management are not generating high class evidence. Recommendations for ICP management requires better designed clinical studies using new concepts to generate interventions according to the new era of personalized medicine.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.135
GPT teacher head0.426
Teacher spread0.292 · 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.

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

Citations31
Published2022
Admission routes1
Has abstractyes

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