Considerations for informing precision psychiatry in eating disorders: Foundations for future practice
Bibliographic record
Abstract
Eating disorders (EDs) are multisystemic, debilitating, and complex illnesses that affect many young Canadians. These disorders are associated with high rates of medical complications, psychiatric and physical comorbidities, functional impairment, family distress, and financial burden. Despite the severity and increasing prevalence of EDs in youth, advancements in understandings of the pathophysiology and treatment of EDs have remained limited over the past three decades. This trend may be shaped by the chronic underfunding of the field, reliance on small sampled cross-sectional studies, and the notable lack of research focused on youth with EDs from historically underrepresented communities. Current treatment practices demonstrate modest efficacy and often omit the complex, heterogeneous presentations, development, and maintenance of pediatric EDs. Large-scale, multiaxial datasets are necessary to elucidate ED etiology and enable phenotyping. This is a critical step towards implementing future precision psychiatry and personalized treatment advances. In this commentary, we share our experience of conceptualizing a precision ED data and bio-registry, EDBioMAP: Eating Disorder Bio-Registry and Multiaxial Precision Health Platform, and suggest necessary pillars to inform, implement, and drive the successful use of precision psychiatry in pediatric ED care. Effective data utilization requires actionable steps and includes: (1) establishing strategic partnerships; (2) incorporating measurement-based care into clinical practice; (3) collecting novel biological markers; (4) developing minimum datasets; and (5) leveraging predictive modelling techniques. Strategic and standardized data integration is imperative to informing the future use of precision psychiatry for EDs. It can lend well to igniting multi-site collaboration to enhance large datasets necessary for this type of work and offers avenues for future development of personalized treatment interventions and clinical decision-making tools for youth with EDs.
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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 | 0.198 | 0.419 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.018 | 0.038 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.025 | 0.053 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".