Weight outcomes for adolescents with atypical anorexia nervosa in family-based treatment.
Bibliographic record
Abstract
Background: Although over one-third of adolescents presenting with restrictive eating disorders have a history of being overweight, there is no evidence-based treatment for atypical anorexia nervosa (AAN). Family-Based Treatment (FBT) is a feasible treatment and is routinely applied to treat atypical anorexia nervosa in adolescents; however, identifying a treatment target weight within FBT for these patients is a challenge. Objective: This study aimed to 1) increase understanding regarding recommendations for weight gain versus weight stabilization in FBT for adolescents with AAN and 2) examine treatment outcomes in FBT for adolescents with AAN. Method: Using a retrospective design, we reviewed the files of 41 patients with AAN who were referred for FBT at a pediatric eating disorder program located within a tertiary care health centre. Results: We found variability in recommendations for weight gain, with 56% of the sample recommended to gain weight and 44% recommended to stabilize weight. Baseline BMI for age appeared to be a key factor in establishing recommendations for weight gain. AAN patients in our sample gained a significant amount of weight across treatment, with those recommended to gain weight showing more weight gain during treatment. Forty-nine percent of the sample completed FBT; those patients displayed a mean of 10kg of weight gain during treatment. Conclusions: Findings suggest that many patients gained weight during the course of FBT for AAN. Further study on weight changes during FBT for adolescents with AAN and increased diagnostic consistency for AAN will be important for this field.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".