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Record W4207014458 · doi:10.1089/neur.2021.0054

Intracranial Pressure–Derived Cerebrovascular Reactivity Indices, Chronological Age, and Biological Sex in Traumatic Brain Injury: A Scoping Review

2022· review· en· W4207014458 on OpenAlexaff
Carleen Batson, Kevin Y. Stein, Alwyn Gomez, Amanjyot Singh Sainbhi, Logan Froese, Arsalan Alizadeh, François Mathieu, Frederick A. Zeiler

Bibliographic record

VenueNeurotrauma Reports · 2022
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of TorontoUniversity of Manitoba
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMedicineTraumatic brain injuryCochrane LibraryWeb of scienceIntracranial pressureScopusMeta-analysisInternal medicineMEDLINESurgeryPsychiatryBiology

Abstract

fetched live from OpenAlex

To date, there has been limited literature exploring the association between age and sex with cerebrovascular reactivity (CVR) in moderate/severe traumatic brain injury (TBI). Given the known link between age, sex, and cerebrovascular function, knowledge of the impacts on continuously assessed CVR is critical for the development of future therapeutics. We conducted a scoping review of the literature for studies that had a direct statistical interrogation of the relationship between age, sex, and continuous intracranial pressure (ICP)-based indices of CVR in moderate/severe TBI. The ICP-based indices researched included: pressure reactivity index (PRx), pulse amplitude index (PAx), and RAC. MEDLINE, BIOSIS, EMBASE, SCOPUS, Global Health, and the Cochrane library were searched from inception to June 2021 for relevant articles. A total of 10 original studies fulfilled our inclusion criteria. Nine of the articles documented a correlation between advanced age and worse CVR, with eight using PRx (2192 total patients), three using PAx (978 total patients), and one using RAC (358 total patients), p < 0.05; R ranging from 0.17 to 0.495 for all indices across all studies. Three articles (1256 total patients) displayed a correlation between biological sex and PRx, with females trending towards higher PRx values ( p < 0.05) in the limited available literature. However, no literature exists comparing PAx or RAC with biological sex. Findings showed that aging was associated with impaired CVR. We observed a trend between female sex and worse PRx values, but the literature was limited and statistical significance was borderline. The identified studies were few in number, carried significant population heterogeneity, and utilized grand averaging of large epochs of physiology during statistical comparisons with age and biological sex. Because of the heterogeneous nature of TBI populations and limited focus on the effects of age and sex on outcomes in TBI, it is challenging to highlight the differences between the indices and patient age groups and sex. The largest study showing an association between PRx and age was done by Zeiler and colleagues, where 165 patients were studied noting that patients with a mean PRx value above zero had a mean age above 51.4 years versus a mean age of 41.4 years for those with a mean PRx value below zero ( p = 0.0007). The largest study showing an association between PRx and sex was done by Czosnyka and colleagues, where 469 patients were studied noting that for patients <50 years of age, PRx was worse in females (0.11 ± 0.047) compared to males (0.044 ± 0.031), p < 0.05. The findings from these 10 studies provide preliminary data, but are insufficient to definitively characterize the impact of age and sex on CVR in moderate/severe TBI. Future work in the field should focus on the impact of age and sex on multi-modal cerebral physiological monitoring.

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)0.008
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0190.018
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.133
GPT teacher head0.376
Teacher spread0.243 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations17
Published2022
Admission routes1
Has abstractyes

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