“You mean it's more than just an eating disorder?”: Commentary on Wade et al. (2023)
Bibliographic record
Abstract
Drawing from literature on measurement-based care and prognostic indices in eating disorder (ED) treatment, Wade et al. offer an algorithm for treating co-occurring mental-health conditions (i.e., psychiatric comorbidity) in people with EDs, and for studying effects of comorbidity-oriented treatments. Advocating session-by-session measurement to inform adaptive treatment, their proposal outlines a process for adjusting conventional ED treatment to situations in which comorbidity impedes treatment progress. The plan is methodical and responsive to evidence suggesting that peoples' early in-treatment change has more power, prognostically, than do indices of comorbidity. In the absence of data to inform practices in some areas, the authors intentionally leave key questions unanswered until future results are in. But this means that they reserve comment on how to determine that comorbidity is interfering with treatment response, or to select the best-fitting of available comorbidity-oriented options. Likewise, the proposal draws most of its inspiration from literature on individual (mainly cognitive-behavioral) psychotherapy and, as a result, does not fully represent biopsychosocial perspectives, or elaborate upon the place in comorbidity management of biological treatments, family, and carer involvement, or more complex integrated approaches. Considerations on how to apply the latter methods would broaden the plan's scope.
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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 | 0.016 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.086 | 0.100 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".