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Record W3163119450 · doi:10.13140/rg.2.2.35052.72327

Examining Causes and Consequences of Mental Health Disorders in Chronic Traumatic Brain Injury

2018· dissertation· en· W3163119450 on OpenAlexfundno aff
Michael J. C. Bray

Bibliographic record

VenueTSpace · 2018
Typedissertation
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersToronto Rehabilitation Institute
KeywordsTraumatic brain injuryMental healthPsychiatryPsychologyMedicineClinical psychology

Abstract

fetched live from OpenAlex

Traumatic brain injury (TBI) is characterized by a host of persisting mental health (cognitive and psychiatric), motor and neurological sequelae, including progressive, degenerative change in the hippocampi. This thesis postulates that hippocampal volume loss may contribute to the increased risk of psychotic disorder that is observed in this population, through dysregulation of dopaminergic networks. The thesis also posits that TBI is implicated in the substantial mental health deficits observed in persons experiencing homelessness. This thesis specifically investigated (1) The relationship between increased psychotic symptom severity and hippocampal volume loss from 5 to 12 months post-injury and (2) The relationship between TBI and cognitive/psychiatric dysfunction among persons experiencing homelessness. A significant association between increasing hippocampal degeneration and increasing psychotic symptom severity was demonstrated. TBI was also demonstrated to bear strong associations with mental health dysfunction among homeless populations.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.449
Teacher spread0.350 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

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