Rethinking the spectrum of mood disorders: implications for diagnosis and management – Proceedings of a symposium presented at the 30th Annual European College of Neuropsychopharmacology Congress, 4 September 2017, Paris, France
Bibliographic record
Abstract
, fifth edition (DSM-5), psychiatric disorders were classified using a categorical approach. However, it was recognized that such an approach was too rigid to encompass the range of symptomatology encountered in clinical practice. Therefore, a dimensional approach was adopted in DSM-5, in which affective states are considered to be distributed across a continuum ranging from pure mania to pure depression. In addition, the copresence of symptoms of the opposite pole are captured using a 'with mixed features' specifier, applied when three or more nonoverlapping subthreshold symptoms of the opposite pole are present. Mixed features are common in patients with mood episodes, complicating the course of illness, reducing treatment response and worsening outcomes. However, research in this area is scarce and treatment options are limited. Current evidence indicates that antidepressants should be avoided for the treatment of bipolar mixed states. Evidence for bipolar mixed states supports the use of several second-generation antipsychotics, valproate and electroconvulsive therapy. One randomized controlled trial has demonstrated the efficacy of lurasidone, compared with placebo, in patients with major depressive disorder with mixed features, and there is limited evidence supporting the use of ziprasidone in such patients. Further research is required to determine whether other antipsychotic agents, or additional therapeutic approaches, might also be effective in this setting.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".