Challenges in defining treatment‐resistant mania in adults: A systematic review
Bibliographic record
Abstract
OBJECTIVES: To review the definitions of treatment-resistant mania (TRM) in the literature and propose criteria for an operationalized definition. METHODS: A systematic search of five databases (MEDLINE, EMBASE, PsychInfo, Cochrane Central, and CINAHL) and data extraction of eligible articles. RESULTS: In total, 47 articles addressing the concept of TRM were included, comprising 16 case reports, 11 case series, 3 randomized clinical trials, 8 open-label clinical trials, 1 experimental study, 7 narrative reviews, and 1 systematic review. While reviews discussed several challenges in defining TRM, definitions varied substantially based on different criteria for severity of mania, duration of mania, and use of specific therapeutic agents with minimal dosages and duration of treatment. Only a handful of the reviewed articles operationalized these criteria. CONCLUSION: While the concept of TRM has been discussed in the literature for over three decades, we could not find an agreed-upon operationalized definition based on specific criteria. We propose and discuss a possible definition that could be used by clinicians to guide their practice and by researchers to assess the prevalence of TRM and develop and test interventions targeting TRM.
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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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".