Beyond the Guidelines for Bipolar Disorder: Practical Issues in Long-Term Treatment with Lithium
Bibliographic record
Abstract
Objective: Several treatment guidelines are available for clinicians working with patients with bipolar disorder (BD), but some of the more nuanced aspects of lithium therapy go beyond the scope of such guidelines. Therefore, in this perspective, our objective was to focus on specific practical issues of lithium treatment, including the selection and initiation of long-term treatment, and management and discontinuation (if indicated) of lithium prophylaxis. Method: We conducted a focused review of the relevant literature on the treatment of BD. Results: Consultation requests to a BD specialty service often relate to issues for which there is limited evidence, including when to initiate long-term treatment, whether choice of mood stabilizer is specific, how long to treat acute episodes, whether to switch or add on medication when treatment fails, how long to continue effective treatment, and what medication to use when a lithium-responsive patient must discontinue lithium. Conclusion: Optimal long-term treatment of BD will require more research as well as better alignment of clinical and training programs. Méthode: Nous avons mené une revue ciblée de la littérature pertinente sur le traitement du TB. Résultats: Les demandes de consultation à un service spécialisé en TB ont souvent trait aux questions pour lesquelles les données probantes sont limitées, notamment, le moment d'initier un traitement à long terme, si le choix d'un régulateur de l'humeur est spécifque, combien de temps traiter les épisodes aigus, s'il faut changer de médicament ou en ajouter lorsque le traitement échoue, combien de temps continuer un traitement efficace, et quel médicament utiliser quand un patient qui répond bien au lithium doit arrêter le lithium. Conclusion: Le traitement à long terme optimal du TB exigera plus de recherche ainsi qu'une meilleure harmonisation des programmes cliniques et de formation.
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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 | 0.057 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".