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Record W4410756383 · doi:10.1051/medsci/2025063

Les premiers succès de la piste immuno-métabolique en psychiatrie de précision

2025· review· fr· W4410756383 on OpenAlexaff
Ophélia Godin, Ariel Frajerman, Aistė Lengvenytė, Jérémy Bernard, Ana C. Andreazza, Bruno Étain, Marion Leboyer

Bibliographic record

Venuemédecine/sciences · 2025
Typereview
Languagefr
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsUniversity of Toronto
FundersAgence Nationale de la Recherche
KeywordsGynecologyMedicine

Abstract

fetched live from OpenAlex

Psychiatric disorders are associated with a high prevalence of cardiovascular and metabolic risk factors such as diabetes, obesity, and metabolic syndrome, as well as specific symptoms: hyperphagia, weight gain, hypersomnia, fatigue, and anhedonia. Non-pharmacological therapeutic strategies (exercise, Mediterranean or ketogenic diets) as well as pharmacological approaches, such as repositioning molecules (metformin or GLP1 receptor agonists), are available and could be proposed to these patients. In addition, targeting immuno-inflammatory pathways may be beneficial for these individuals.

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)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.030
GPT teacher head0.371
Teacher spread0.341 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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