Personalised and precision mental health in eating disorders: why routine outcome measurement is key
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
For over a decade, the mental health field has been interested in precision treatment using psychopharmacological interventions. More recently, this interest has expanded to include psychotherapy, which is the primary treatment modality for eating disorders. Personalised medicine and precision treatment are also seen as priorities for the eating disorder field by those with lived experience and carers, clinicians and researchers. However, precision treatment necessitates the collection of large amounts of clinical data. Three frameworks exist or have been proposed for the purpose of gathering large-scale routine clinical outcomes in eating disorder services: The International Consortium for Health Outcomes Measurement (ICHOM) eating disorder set, the Australia national minimum dataset, and the Eating Disorders Clinical Research Network. Despite the emergence of these frameworks, challenges exist with implementation. This paper outlines the rationale for the collection of routine outcome data in eating disorder treatment settings, the three existing frameworks proposed, and considerations for implementation and scaling. These include clinical and practice applications, technical aspects, statistics, and contextual factors. We invite attention to our recommendations and collaborative approaches to facilitate progress towards precision treatment in eating disorders.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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