Establishment of the Pediatric Obesity Weight Evaluation Registry: A National Research Collaborative for Identifying the Optimal Assessment and Treatment of Pediatric Obesity
Bibliographic record
Abstract
BACKGROUND: Prospective patient registries have been successfully utilized in several disease states with a goal of improving treatment approaches through multi-institutional collaboration. The prevalence of youth with severe obesity is at a historic high in the United States, yet evidence to guide effective weight management is limited. The Pediatric Obesity Weight Evaluation Registry (POWER) was established in 2013 to identify and promote effective intervention strategies for pediatric obesity. METHODS: Sites in POWER provide multicomponent pediatric weight management (PWM) care for youth with obesity and collect a defined set of demographic and clinical parameters, which they regularly submit to the POWER Data Coordinating Center. A program profile survey was completed by sites to describe characteristics of the respective PWM programs. RESULTS: From January 2014 through December 2015, 26 US sites were enrolled in POWER and had submitted data on 3643 youth with obesity. Ninety-five percent were 6-18 years of age, 54% female, 32% nonwhite, 32% Hispanic, and 59% publicly insured. Over two-thirds had severe obesity. All sites included a medical provider and used weight status in their referral criteria. Other program characteristics varied widely between sites. CONCLUSION: POWER is an established national registry representing a diverse sample of youth with obesity participating in multicomponent PWM programs across the United States. Using high-quality data collection and a collaborative research infrastructure, POWER aims to contribute to the development of evidence-based guidelines for multicomponent PWM programs.
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 | 0.094 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".