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Record W3106920291 · doi:10.1038/s41380-020-00951-9

The kynurenine pathway in major depressive disorder, bipolar disorder, and schizophrenia: a meta-analysis of 101 studies

2020· review· en· W3106920291 on OpenAlexaff
Wolfgang Marx, Amelia J. McGuinness, Tetyana Rocks, Anu Ruusunen, Jasmine Cleminson, Adam J. Walker, Susana Gomes‐da‐Costa, Melissa M. Lane, Marsal Sanches, Alexandre Paim Díaz, Ping‐Tao Tseng, Pao‐Yen Lin, Michael Berk, Gerard Clarke, Adrienne O’Neil, Felice N. Jacka, Brendon Stubbs, André F. Carvalho, João Quevedo, Jair C. Soares, Brisa S. Fernandes

Bibliographic record

VenueMolecular Psychiatry · 2020
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute for Health and Care Research
KeywordsKynurenic acidQuinolinic acidKynurenineKynurenine pathwayMajor depressive disorderBipolar disorderSchizophrenia (object-oriented programming)PsychologyInternal medicineMood disordersEndocrinologyPharmacologyPsychiatryMoodMedicineTryptophanChemistryBiochemistryAmino acid

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.016
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.048
GPT teacher head0.321
Teacher spread0.272 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations306
Published2020
Admission routes1
Has abstractno

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