A single-center experience with service organization for patients with ARFID
Bibliographic record
Abstract
Abstract Background To date, very little research has explored the impact that the newly articulated diagnosis Avoidant restrictive food intake disorder (ARFID) has had on feeding and eating disorder program service organization and delivery. The purpose of this paper is to provide a descriptive overview of a single-center, ARFID-specific pilot clinic that sought to better understand the specific needs of patients with ARFID and gain insight into treatment requirements. Methods A retrospective cohort study was completed on patients with ARFID admitted to a specialized pilot clinic within a tertiary care hospital. Results Over an 18 month period, a total of 31 patients were assessed, with 26 patients completing follow-up assessments. Patients presented with heterogeneous manifestations of ARFID, with treatment plans tailored to meet individual needs at assessment and over the treatment period. A multidisciplinary approach was most often administered, including a combination of administered individual therapy, family therapy, medical monitoring, and prescribed medications. Only 30% of patients were treated exclusively by therapists on the eating disorder team. Conclusions The experiences gained from this pilot study highlight the need for specialized resources for assessment and treatment of patients with ARFID, the importance of a multidisciplinary approach to treatment, and the necessity of utilization of ARFID-specific measures for program evaluation purposes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".