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Record W2042156043 · doi:10.1080/02699050802084886

The serotonin transporter polymorphisms and major depression following traumatic brain injury

2008· article· en· W2042156043 on OpenAlexafffund
Florance Chan, Krista L. Lanctôt, Anthony Feinstein, Nathan Herrmann, John S. Strauss, Tricia Sicard, James L. Kennedy, Scott McCullagh, Mark Rapoport

Bibliographic record

VenueBrain Injury · 2008
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSunnybrook Health Science CentreHealth Sciences Centre
FundersOntario Neurotrauma Foundation
KeywordsSerotonin transporterHamdDepression (economics)Traumatic brain injuryMood disordersMood5-HTTLPRMedicinePsychologyHamilton Rating Scale for DepressionInternal medicineRating scalePsychiatryMajor depressive disorderSerotoninAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to examine the role of the serotonin transporter gene polymorphisms on the risk of major depression following traumatic brain injury (TBI). METHODS: Seventy-five patients who had sustained a TBI and who met the Diagnostic and Statistical Manual of Mental Disorders (4th ed) (DSM-IV) criteria for mood disorder due to TBI were compared to 99 controls with TBI but no mood disorder. The severity of depression was rated using the Hamilton Depression Rating Scale (HAMD) for the depressed patients. All patients were genotyped for the serotonin transporter gene-linked polymorphic region (5-HTTLPR) with the assay for the rs25531 allelic variant. RESULTS: The distribution of genotype frequencies was not different between the depressed and control groups (chi(2) = 1.43, df = 2, p = 0.488) and for the depressed patients there was no association between HAMD scores and the polymorphisms (t-test = 1.71, df = 68, p = 0.092). CONCLUSION: There was no evidence of association between the serotonin transporter gene polymorphisms and depression post-TBI. Future research is indicated into the possible role of other candidate genes as risk factors for depression in this population.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.049
GPT teacher head0.330
Teacher spread0.281 · 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 teacher head, not a consensus.

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

Citations36
Published2008
Admission routes2
Has abstractyes

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