sj-docx-2-cpa-10.1177_07067437221147443 - Supplemental material for Neuropsychiatric Symptom Burden across Neurodegenerative Disorders and its Association with Function
Bibliographic record
Abstract
Supplemental material, sj-docx-2-cpa-10.1177_07067437221147443 for Neuropsychiatric Symptom Burden across Neurodegenerative Disorders and its Association with Function by Daniel Kapustin, Shadi Zarei, Wei Wang, Malcolm A. Binns, Paula M. McLaughlin, Agessandro Abrahao, Sandra E. Black, Michael Borrie, David Breen, Leanna Casaubon, Dar Dowlatshahi, Elizabeth Finger, Corinne E Fischer, Andrew Frank, Morris Freedman, David Grimes, Ayman Hassan, Mandar Jog, Donna Kwan, Anthony Lang, Brian Levine, Jennifer Mandzia, Connie Marras, Mario Masellis, Joseph B. Orange, Stephen Pasternak, Alicia Peltsch, Bruce G. Pollock, Tarek K. Rajji, Angela Roberts, Demetrios Sahlas, Gustavo Saposnik, Dallas Seitz, Christen Shoesmith, Alisia Southwell, Thomas D.L. Steeves, Kelly Sunderland, Richard H Swartz, Brian Tan, David F. Tang-Wai, Maria Carmela Tartaglia, Angela Troyer, John Turnbull, Lorne Zinman, and Sanjeev Kumar 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.924 | 0.777 |
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".