Considerations for informing precision psychiatry in eating disorders: Foundations for future practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it