The Short Treatment Allocation Tool for Eating Disorders: current practices in assigning patients to level of care
Bibliographic record
Abstract
The Short Treatment Allocation Tool for Eating Disorders ( STATED) is a new evidence-based algorithm developed to match patients to the most clinically appropriate and cost-effective level of care (Geller et al., 2016). The objective of this research was to examine the extent to which current practices are in alignment with STATED recommendations. Participants were 179 healthcare professionals providing care for youth and/or adults with eating disorders. They completed an online survey and rated the extent to which three patient dimensions ( medical stability , symptom severity , and readiness ) were used in assigning patients to each of five levels of care . The majority of analyses testing a priori hypotheses based on the STATED were statistically significant (all p ’s < .001), in the direction of STATED recommendations. However, a strict coding scheme evaluating the extent to which ratings were fully consistent with the STATED showed inconsistency rates ranging from 17 to 55% across the five levels of care, with the greatest inconsistencies involving the use of readiness information, and the lowest involving the use of medical stability information. Although practices were generally aligned with the STATED recommendations , readiness information was used least consistently in assigning patients to level of care.
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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 | 0.051 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".