MétaCan
Menu
Back to cohort
Record W1975717160 · doi:10.1016/j.eurpsy.2009.12.013

Gene expression profiling of suicide completers

2010· review· en· W1975717160 on OpenAlex
Laura M. Fiori, Gustavo Turecki

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

VenueEuropean Psychiatry · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsProfiling (computer programming)Gene expression profilingPsychologyGene expressionComputational biologyMedicineGeneGeneticsBiologyComputer science

Abstract

fetched live from OpenAlex

Despite strong evidence for a role of biological factors in the etiology and pathology of suicide, the study of traditional neurotransmitter systems has been able to explain only a small proportion of the neurobiology of what is now recognized as a complex genetic trait. The use of microarrays to simultaneously examine the expression levels of thousands of gene transcripts has vastly expanded our capacity to detect the involvement of additional genes and pathways in suicidality, and has opened many new avenues for the discovery of the biological underpinnings of suicide completion. This review examines microarray studies which have been used to identify genes displaying altered expression in suicide completers, and highlights some of the important methodological considerations and metabolic pathways which have emerged from these analyses.

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 score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.036
GPT teacher head0.323
Teacher spread0.286 · 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