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Record W4386315625 · doi:10.1007/s13311-023-01428-7

Neurosteroid Receptor Modulators for Treating Traumatic Brain Injury

2023· review· en· W4386315625 on OpenAlexaff
Todd A. Verdoorn, Tom J. Parry, Graziano Pinna, Jonathan Lifshitz

Bibliographic record

VenueNeurotherapeutics · 2023
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsOntario Neurotrauma Foundation
Fundersnot available
KeywordsTraumatic brain injuryNeuroactive steroidNeuroscienceNeurologyMedicineDrug developmentDrugNeurovascular bundleInflammationBioinformaticsPharmacologyReceptorPsychologyInternal medicineBiologyPathologyPsychiatryGABAA receptor

Abstract

fetched live from OpenAlex

Traumatic brain injury (TBI) triggers wide-ranging pathology that impacts multiple biochemical and physiological systems, both inside and outside the brain. Functional recovery in patients is impeded by early onset brain edema, acute and chronic inflammation, delayed cell death, and neurovascular disruption. Drug treatments that target these deficits are under active development, but it seems likely that fully effective therapy may require interruption of the multiplicity of TBI-induced pathological processes either by a cocktail of drug treatments or a single pleiotropic drug. The complex and highly interconnected biochemical network embodied by the neurosteroid system offers multiple options for the research and development of pleiotropic drug treatments that may provide benefit for those who have suffered a TBI. This narrative review examines the neurosteroids and their signaling systems and proposes directions for their utility in the next stage of TBI drug research and development.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.167
GPT teacher head0.400
Teacher spread0.233 · 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 designNot applicable
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

Citations13
Published2023
Admission routes1
Has abstractyes

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