Extending the specificity of mood stabilizers from clinical response to mortality reduction
Bibliographic record
Abstract
Extending the specificity of mood stabilizers from clinical response to mortality reduction Mood stabilizers are a heterogeneous class of drugs including pharmacological agents with diverse mechanisms of actions (anticonvulsants, second generation antipsychotics, and lithium) but sharing one fundamental clinical property: the ability to treat and prevent episode recurrences in patients affected by major affective disorders.An attempt to formulate some evidence-based taxonomic criteria suggested that an ideal mood stabilizer should be effective in: (a) treating acute manic symptoms; (b) treating acute depressive symptoms; (c) preventing manic symptoms; and (d) preventing depressive symptoms.1 Lithium is probably the only mood stabilizer satisfying all these criteria, although it is less effective in treating bipolar depression. 2 Similarly, the clinical profile of anticonvulsants appears to be specific depending on the illness phase (acute and/or continuation/maintenance) and on the mood polarity.Indeed, lamotrigine is more effective in preventing depression, but not mania, and possibly in treating acute bipolar depression and rapid cycling.3 Conversely, carbamazepine and valproic acid are effective in the treatment of acute mania, as well as in maintenance.3,4 The latter also shows more effectiveness in certain subgroups of patients presenting with mixed mania or mania with irritability, compared with other treatments.4,5 Finally, antipsychotics also show effectiveness in preventing mood relapses, 6 with possibly higher efficacy in treating acute mixed episodes in bipolar disorder.7 These patterns of efficacy/effectiveness appear to be associated with distinct clinical presentations that also show a degree of predictive power.This has indeed been demonstrated for lithium, where specific clinical characteristics, namely an episodic (mania-depression-interval) clinical course sequence, absence of rapid cycling, absence of psychotic symptoms, family history of bipolar disorder, shorter pre-lithium illness duration and later age of onset, 8 predict a good clinical response to lithium.Interestingly, a recent machine learning study, evaluated whether lithium responsiveness was predictable using clinical markers in a large multicenter sample of patients with bipolar disorder.9 The authors showed that lithium responsiveness was predictable in the pooled sample with good accuracy area under the receiver operating characteristic curve of 0.80 and a particularly low false-positive rate (0.91). 9 More
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".