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Record W2009439040 · doi:10.1159/000356549

Targeting the Inflammatory Pathway as a Therapeutic Tool for Major Depression

2014· review· en· W2009439040 on OpenAlexaff
Cristiano Noto, Lucas B. Rizzo, Rodrigo B. Mansur, Roger S. McIntyre, Michaël Maes, Elisa Brietzke

Bibliographic record

VenueNeuroImmunoModulation · 2014
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMajor depressive disorderDepression (economics)Inflammatory responseMedicineBipolar disorderInflammationPsychologyBioinformaticsPsychiatryImmunologyCognitionBiology

Abstract

fetched live from OpenAlex

In the last decades convergent findings from several lines of evidence has revealed a robust association between major depressive disorder (MDD) and inflammatory pathways. Despite this, the translation of these findings into new and better treatments for MDD has not occurred. The objective of this study is to comprehensively review what is already known with reasonable certainty on inflammatory pathways in MDD, to clarify some points that have been insufficiently studied and to discuss the implications of these findings for future studies targeting inflammatory pathways as a therapeutic tool for individuals with MDD.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.305
Teacher spread0.265 · 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 designNot applicable
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

Citations38
Published2014
Admission routes1
Has abstractyes

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