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Record W4230521399 · doi:10.22215/etd/2016-11651

Cognitive, Experiential and Genetic Contribution to Depressive Symptoms in Male and Female Students with a History of Concussion

2016· dissertation· en· W4230521399 on OpenAlexaff
Kaylyn Dixon

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsConcussionCognitionClinical psychologyPsychologyDepression (economics)Cognitive vulnerabilityDepressive symptomsAnxietyPsychiatryMedicineInjury preventionPoison control

Abstract

fetched live from OpenAlex

Mild traumatic brain injuries, or concussions, are generally accompanied by a variety of somatic, cognitive and affective symptoms, including depressive-like symptoms.Although for most individuals, the affective symptoms are relatively transient, for others (~ 17 to 44%), these symptoms can persist for extended periods of time.The longer-lasting symptomology appears to depend, in part, on gender, age, prior concussion history and symptom presentation.Accordingly, the purpose of the present study was to examine several cognitive, genetic, and experiential factors which might be associated with depressive pathology among males and females with and without a history of concussion.To this end, male and female university students, ranging in age from 17 to 25, with a history of concussions (n = 105) and a control group of "never-concussed" individuals (n = 214) completed the Wisconsin Card Sorting Task (WCST) to assess executive function as well as several questionnaires assessing cognitive vulnerabilities to depression, early life v COMT val 158 met .....................

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)0.001
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.020
GPT teacher head0.343
Teacher spread0.323 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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