Trouble bipolaire à début précoce: Validation par les études de mélange et les biomarqueurs
Bibliographic record
Abstract
OBJECTIVES: Bipolar affective disorder (BD) is a multifactorial disorder with heterogeneous clinical presentations, in particular according to age at onset (AAO). The relevance of such an indicator has been discussed as a potential specifier in future nosographical classification. METHOD: We summarize available evidence of admixture analyses and biomarkers in early onset BD. RESULTS: Numerous clinical arguments have led us to conclude that the early onset BD subgroup is clinically homogeneous, with particular, recurrent, and severe characteristics.Eight admixture studies have demonstrated the existence of 3 subgroups of patients with BD according to AAO (early, intermediate, and late AAO), with 2 cut-off points of 21 (21.33) [SD 1.41]) and 35 years (34.67 [SD 5.52]). Differential clinical features and outcome measures characterize the early onset subgroup: higher rate of suicide attempts, rapid cycling, alcohol and drugs misuse, psychotic symptoms, and comorbid anxiety disorders. This may partially explain the delayed diagnosis and late initiation of mood stabilizers. Genetic, biological, imaging, and cognitive arguments may be considered as potential markers in providing external validity of the existence of this early onset subgroup. Implementation of AAO in the algorithms of treatment may be discussed, although the level of proof for focused medication strategies remains to be consolidated. CONCLUSION: Given the high frequency (44.80%) of early onset BD, awareness of clinicians should be stimulated to provide an early and accurate detection, preventive strategies, and possibly specific treatments.The forthcoming DSM-5 should include AAO as a specifier, given its relevance for course and outcome.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".