Are youths with disruptive mood dysregulation disorder different from youths with major depressive disorder or persistent depressive disorder?
Bibliographic record
Abstract
BACKGROUND: Although the disruptive mood dysregulation disorder (DMDD) was included in the depressive disorders (DD) section of the DSM-5, common and distinctive features between DMDD and the pre-existing DD (i.e., major depressive disorder, MDD, and persistent depressive disorder, PDD) received little scrutiny. METHODS: Youths consecutively assessed as outpatients at two Canadian mood clinics over four years were included in the study (n = 163; mean age:13.4 ± 0.3; range:7-17). After controlling for inter-rater agreement, data were extracted from medical charts, using previously validated chart-review instruments. RESULTS: Twenty-two percent of youths were diagnosed with DMDD (compared to 36% for MDD and 25% for PDD), with substantial overlap between the three disorders. Youths with DMDD were more likely to have a comorbid non-depressive psychiatric disorder - particularly attention deficit hyperactivity disorder, odds ratio (OR=3.9), disruptive, impulse-control and conduct disorder (OR=3.0) or trauma- and stressor-related disorder (OR=2.5). Youths with DMDD did not differ with regard to the level of global functioning, but reported more school and peer-relationship difficulties compared to MDD and/or PDD. The vulnerability factors associated with mood disorders (i.e., history of parental depression and adverse life events) were found at a comparable frequency across the three groups. LIMITATIONS: The retrospective design and the selection bias for mood disordered patients restricted the generalizability of the results. CONCLUSIONS: Youths with DMDD share several clinical features with youths with MDD and PDD. Further studies are required to determine the developmental trajectories and the benefits of expanding pharmacotherapy for DD to DMDD.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".