Making Gains in Eating Disorders Outcomes Research
Bibliographic record
Abstract
* Abbreviations: AN — : anorexia nervosa AAN — : atypical anorexia nervosa EDE-Q — : Eating Disorder Examination Questionnaire mBMI — : median BMI HCR — : higher-calorie refeeding LCR — : lower-calorie refeeding In this issue of Pediatrics , Golden et al1 present 1-year follow-up data from a randomized controlled trial in which researchers examined differences between higher-calorie refeeding (HCR) and lower-calorie refeeding (LCR) during initial hospitalization for the treatment of anorexia nervosa (AN) and atypical anorexia nervosa (AAN) in 111 youth. The initial study demonstrated that HCR restored medical stability earlier and safely, as compared to LCR.2 Of note, both groups reached the threshold required for medical stability on average by 10 days, and patients who received HCR had hospital stays on average 4 days shorter than those in the LCR group, amounting to nearly $20 000 saved per patient.2 Although overall hospitalization rates of youth with AN in the United States are scant, census data and conservative eating disorder prevalence modeling suggest that cost savings associated with the use of HCR could result in tens of millions of health care dollars saved annually when compared to LCR.3–5 Given recent estimates that direct overall … Address correspondence to Mark L. Norris, MD, Department of Pediatrics, Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada K1S 2C8. E-mail: mnorris{at}cheo.on.ca
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.104 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".