The Utility of Research Domain Criteria in Diagnosis and Management of Dual Disorders: A Mini-Review
Bibliographic record
Abstract
The Research Domain Criteria (RDoC) initiative has been considered a comprehensive alternative classification framework for understanding neuropsychiatric ailments, as opposed to the longstanding, traditional DSM framework. Where the DSM categorizes neuropsychiatric disorders as each being distinct and diagnostically defined by the presence of specified symptoms, RDoC provides a multidimensional conceptualization of psychiatric disorders with neurobiological roots. By taking a multidimensional approach, RDoC overcomes two major constraints of the DSM framework: that is, that the DSM is categorical in its approach to psychiatric disorders to the point of understating the intersectionality between concomitant disorders, and that the DSM focuses mainly on clinical features. RDoC seems to better account for the intersection between dual disorders and considers a range of factors, from the more microscopic (e.g., genetics or molecular functions) to the more macroscopic (e.g., environmental influences). The multidimensional approach of RDoC is particularly appealing in the context of dual disorders. Dual disorders refers to a concurrent psychiatric disorder with an addiction disorder. RDoC accounts for the fact that there is often overlap in symptoms across and bidirectional influence between various disorders. However, to date, there is limited research into the clinical utility of RDoC, and less so in the context of the clinical management of dual disorders. In this Mini-Review, we discuss how RDoC differs from the DSM, what outcomes have been reported in utilizing RDoC clinically, the utility of RDoC for the diagnosis, management, and monitoring of psychopathology, and the limitations of RDoC as well as avenues for future research.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".