A Delphi consensus among experts on assessment and treatment of disruptive mood dysregulation disorder
Bibliographic record
Abstract
Objective: The aim of this study was to explore consensus among clinicians and researchers on how to assess and treat Disruptive Mood Dysregulation Disorder (DMDD). Methods: The Delphi method was used to organize data collected from an initial sample of 23 child psychiatrists and psychologists. Three rounds of closed/open questions were needed to achieve the objective. Results: Fifteen experts in the field completed the whole study. Finally, 122 proposals were validated and 5 were rejected. Globally, consensus was more easily reached on items regarding assessment than on those regarding treatment. Specifically, experts agreed that intensity, frequency, and impact of DMDD symptoms needed to be measured across settings, including with parents, siblings, peers, and teachers. While a low level of consensus emerged regarding optimal pharmacological treatment, the use of psychoeducation, behavior-focused therapies (e.g., dialectical behavior therapy, chain analysis, exposure, relaxation), and systemic approaches (parent management training, family therapy, parent-child interaction therapy) met with a high degree of consensus. Conclusion: This study presents recommendations that reached a certain degree of consensus among researchers and clinicians regarding the assessment and treatment of youths with DMDD. These findings may be useful to clinicians working with this population and to researchers since they also highlight non-consensual areas that need to be further investigated.
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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 | 0.243 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".