The CINP Guidelines on the Definition and Evidence-Based Interventions for Treatment-Resistant Bipolar Disorder
Bibliographic record
Abstract
BACKGROUND: Resistant bipolar disorder is a major mental health problem related to significant disability and overall cost. The aim of the current study was to perform a systematic review of the literature concerning (1) the definition of treatment resistance in bipolar disorder, (2) its clinical and (3) neurobiological correlates, and (4) the evidence-based treatment options for treatment-resistant bipolar disorder and for eventually developing guidelines for the treatment of this condition. MATERIALS AND METHODS: The PRISMA method was used to identify all published papers relevant to the definition of treatment resistance in bipolar disorder and the associated evidence-based treatment options. The MEDLINE was searched to April 22, 2018. RESULTS: Criteria were developed for the identification of resistance in bipolar disorder concerning all phases. The search of the literature identified all published studies concerning treatment options. The data were classified according to strength, and separate guidelines regarding resistant acute mania, acute bipolar depression, and the maintenance phase were developed. DISCUSSION: The definition of resistance in bipolar disorder is by itself difficult due to the complexity of the clinical picture, course, and treatment options. The current guidelines are the first, to our knowledge, developed specifically for the treatment of resistant bipolar disorder patients, and they also include an operationalized definition of treatment resistance. They were based on a thorough and deep search of the literature and utilize as much as possible an evidence-based approach.
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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 | 0.031 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".