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Translocator Protein Distribution Volume Predicts Reduction of Symptoms During Open-Label Trial of Celecoxib in Major Depressive Disorder

2020· article· en· W3014073663 on OpenAlexafffund
Sophia Attwells, Elaine Setiawan, Pablo Rusjan, Cynthia Xu, Celeste Hutton, Dorsa Rafiei, Benjamin Varughese, Alan Kahn, Stephen J. Kish, Neil Vasdev, Sylvain Houle, Jeffrey H. Meyer

Bibliographic record

VenueBiological Psychiatry · 2020
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Center for Research ResourcesNational Institute of Mental HealthNational Institutes of HealthJanssen PharmaceuticalsNational Institute on Drug AbuseBrain and Behavior Research FoundationCanada Research ChairsAzrieli FoundationCanada Foundation for InnovationCanadian Institutes of Health ResearchJazz Pharmaceuticals
KeywordsTranslocator proteinGliosisCelecoxibInternal medicineMedicineMajor depressive disorderPositron emission tomographyAnterior cingulate cortexPathologyPsychiatryNuclear medicineDiseaseCognition

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.275
Teacher spread0.238 · 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 designNon-randomized trial
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

Citations60
Published2020
Admission routes2
Has abstractno

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