sj-docx-1-cpa-10.1177_07067437241245384 - Supplemental material for Canadian Network for Mood and Anxiety Treatments (CANMAT) 2023 Update on Clinical Guidelines for Management of Major Depressive Disorder in Adults: Réseau canadien pour les traitements de l'humeur et de l'anxiété (CANMAT) 2023 : Mise à jour des lignes directrices cliniques pour la prise en charge du trouble dépressif majeur chez les adultes
Bibliographic record
Abstract
Supplemental material, sj-docx-1-cpa-10.1177_07067437241245384 for Canadian Network for Mood and Anxiety Treatments (CANMAT) 2023 Update on Clinical Guidelines for Management of Major Depressive Disorder in Adults: Réseau canadien pour les traitements de l'humeur et de l'anxiété (CANMAT) 2023 : Mise à jour des lignes directrices cliniques pour la prise en charge du trouble dépressif majeur chez les adultes by Raymond W. Lam, Sidney H. Kennedy, Camelia Adams, Anees Bahji, Serge Beaulieu, Venkat Bhat, Pierre Blier, Daniel M. Blumberger, Elisa Brietzke, Trisha Chakrabarty, André Do, Benicio N. Frey, Peter Giacobbe, David Gratzer, Sophie Grigoriadis, Jeffrey Habert, M. Ishrat Husain, Zahinoor Ismail, Alexander McGirr, Roger S. McIntyre, Erin E. Michalak, Daniel J. Müller, Sagar V. Parikh, Lena S. Quilty, Arun V. Ravindran, Nisha Ravindran, Johanne Renaud, Joshua D. Rosenblat, Zainab Samaan, Gayatri Saraf, Kathryn Schade, Ayal Schaffer, Mark Sinyor, Claudio N. Soares, Jennifer Swainson, Valerie H. Taylor, Smadar V. Tourjman, Rudolf Uher, Michael van Ameringen, Gustavo Vazquez, Simone Vigod, Daphne Voineskos, Lakshmi N. Yatham and Roumen V. Milev in The Canadian Journal of Psychiatry
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.936 | 0.803 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".