Characteristics and clinical trajectories of patients meeting criteria for avoidant/restrictive food intake disorder that are subsequently reclassified as anorexia nervosa
Bibliographic record
Abstract
OBJECTIVE: To examine the initial assessment profiles and early treatment trajectories of youth meeting the criteria for avoidant/restrictive food intake disorder (ARFID) that were subsequently reclassified as anorexia nervosa (AN). METHOD: A retrospective cohort study of patients assessed and treated in a tertiary care eating disorders (ED) program was completed. RESULTS: Of the 77 included patients initially meeting criteria for ARFID, six were reclassified as having AN (7.8%) at a median rate of 71 days after the first assessment. Patients in this cohort presented at very low % treatment goal weight (median 71.6%), self-reported abbreviated length of illness (median 6 months), and exhibited low resting heart rates (median 46 beats per minute). Nutrition and feeding focused worries related more to general health as opposed to specific weight and shape concerns or fears at assessment in half of those reclassified with AN. Treatment at the 6-month mark varied among patients, but comprised family and individual therapy, as well as prescription of psychotropic medication. CONCLUSION: Prospective longitudinal research that utilizes ARFID-specific as well as traditional eating disorder diagnostic measures is required to better understand how patients with restrictive eating disorders that deny fear of weight gain can be differentiated and best treated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".