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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 OpenAlex
Todd A. Verdoorn, Tom J. Parry, Graziano Pinna, Jonathan Lifshitz

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.167
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