Treatment-emergent mania with psychosis in bipolar depression with left intermittent theta-burst rTMS
Bibliographic record
Abstract
To our knowledge, this is the first case report of TEM with psychosis precipitated by iTBS applied to the left DLPFC in bipolar depression that has been reported in the literature. With the recent approval of iTBS for major depressive disorder by the FDA in 2018 [[5]MagVenture Another FDA clearance for MagVenture: 3 minute depression treatment n.d.https://www.magventure.com/component/k2/5-news/124-fda-clearance-for-magventureGoogle Scholar], it is likely that increasing numbers of patients with depression – including off-label use in patients with bipolar depression – will receive iTBS in the coming years. This case report highlights the potential risk for iTBS to precipitate mania in patients with an established bipolar disorder diagnosis, even when receiving treatment with mood stabilizing agents [[1]Yatham L.N. Kennedy S.H. Parikh S.V. Schaffer A. Bond D.J. Frey B.N. et al.Canadian network for mood and anxiety treatments (CANMAT) and international society for bipolar disorders (ISBD) 2018 guidelines for the management of patients with bipolar disorder.Bipolar Disord. 2018; 20: 97-170https://doi.org/10.1111/bdi.12609Crossref PubMed Scopus (752) Google Scholar].
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".