The International College of Neuro-Psychopharmacology (CINP) Treatment Guidelines for Bipolar Disorder in Adults (CINP-BD-2017), Part 2: Review, Grading of the Evidence, and a Precise Algorithm
Bibliographic record
Abstract
Background: The current paper includes a systematic search of the literature, a detailed presentation of the results, and a grading of treatment options in terms of efficacy and tolerability/safety. Material and Methods: The PRISMA method was used in the literature search with the combination of the words 'bipolar,' 'manic,' 'mania,' 'manic depression,' and 'manic depressive' with 'randomized,' and 'algorithms' with 'mania,' 'manic,' 'bipolar,' 'manic-depressive,' or 'manic depression.' Relevant web pages and review articles were also reviewed. Results: The current report is based on the analysis of 57 guideline papers and 531 published papers related to RCTs, reviews, posthoc, or meta-analysis papers to March 25, 2016. The specific treatment options for acute mania, mixed episodes, acute bipolar depression, maintenance phase, psychotic and mixed features, anxiety, and rapid cycling were evaluated with regards to efficacy. Existing treatment guidelines were also reviewed. Finally, Tables reflecting efficacy and recommendation levels were created that led to the development of a precise algorithm that still has to prove its feasibility in everyday clinical practice. Conclusions: A systematic literature search was conducted on the pharmacological treatment of bipolar disorder to identify all relevant random controlled trials pertaining to all aspects of bipolar disorder and graded the data according to a predetermined method to develop a precise treatment algorithm for management of various phases of bipolar disorder. It is important to note that the some of the recommendations in the treatment algorithm were based on the secondary outcome data from posthoc analyses.
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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 | 0.060 | 0.106 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.032 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".