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Record W4401057272 · doi:10.1038/s44220-024-00301-6

Publisher Correction: Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

2024· article· en· W4401057272 on OpenAlexaff
Cynthia H.Y. Fu, Mathilde Antoniades, Güray Erus, José García, Yong Fan, Danilo Arnone, Stephen R. Arnott, Ki Sueng Choi, Cherise Chin Fatt, Benício N. Frey, Vibe G. Frøkjær, Melanie Ganz, Beata R. Godlewska, Stefanie Hassel, Keith Ho, Andrew M. McIntosh, Kun Qin, Susan Rotzinger, Matthew D. Sacchet, Jonathan Savitz, Haochang Shou, Ashish Singh, Aleks Stolicyn, Irina A. Strigo, Stephen C. Strother, Duygu Tosun, Teresa A. Victor, Dongtao Wei, Toby Wise, Roland Zahn, Ian Anderson, W. Edward Craighead, J.F.W. Deakin, Boadie W. Dunlop, Rebecca Elliott, Qiyong Gong, Ian H. Gotlib, Catherine J. Harmer, Sidney H. Kennedy, Gitte M. Knudsen, Helen S. Mayberg, Martin P. Paulus, Jiang Qiu, Madhukar H. Trivedi, Heather C. Whalley, Chao‐Gan Yan, Allan H. Young, Christos Davatzikos

Bibliographic record

VenueNature Mental Health · 2024
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity of TorontoUniversity Health NetworkMcMaster UniversityUniversity of CalgarySt. Joseph’s Healthcare HamiltonBaycrest Hospital
Fundersnot available
KeywordsAntidepressantMajor depressive disorderPlaceboAntidepressant medicationPsychiatryPlacebo responsePsychologyMedicineDepressive symptomsDepression (economics)Alternative medicineCognitionAnxiety

Abstract

fetched live from OpenAlex

In the version of the article initially published, the first affiliation of Melanie Ganz was incorrect and has now been amended to Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark in the HTML and PDF versions of the article.

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)0.004
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.072
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0040.002
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0490.022

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 head0.010
GPT teacher head0.321
Teacher spread0.311 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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