Beyond evidence‐based treatment of bipolar disorder: Rational pragmatic approaches to management
Bibliographic record
Abstract
The evidence for efficacy of many currently available treatments for bipolar disorder is based on studies of nonrefractory patients with bipolar disorder. Therefore, not surprisingly, most treatment recommendations and guidelines for the treatment of bipolar disorder and its many comorbidities depend heavily on data from placebo controlled randomized clinical trials (RCTs), but these RCTs provide little direction for the clinician as to what next steps might be optimal in non- or partial-responders and in those with ongoing medical and psychiatric comorbidities. Given this and the paucity of RCTs at later treatment junctures, we thought it appropriate to begin a discussion of the quality of the data that some experts in the field might consider using in choosing and sequencing drugs and their combination. We acknowledge that many other clinical investigators may prefer very different sequences, but thought the suggestions offered here might be useful to some clinicians in the field, might start discussions of other options in the literature, and, at the same time, provide a preliminary outline for a new round of much-needed clinical trials to better inform clinical practice. Given the very wide range of the quality of the data and clinical principles on which the current suggestions are based, only minimal references are included and a comprehensive review of the literature supporting each option would be outside the scope of this manuscript.
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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 | 0.045 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".