Brief Primary Care Obesity Interventions: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Although practice guidelines suggest that primary care providers working with children and adolescents incorporate BMI surveillance and counseling into routine practice, the evidence base for this practice is unclear. OBJECTIVE: To determine the effect of brief, primary care interventions for pediatric weight management on BMI. DATA SOURCES: Medline, CENTRAL, Embase, PsycInfo, and CINAHL were searched for relevant publications from January 1976 to March 2016 and cross-referenced with published studies. STUDY SELECTION: Eligible studies were randomized controlled trials and quasi-experimental studies that compared the effect of office-based primary care weight management interventions to any control intervention on percent BMI or BMI z scores in children aged 2 to 18 years. DATA EXTRACTION: Two reviewers independently screened sources, extracted data on participant, intervention, and study characteristics, z-BMI/percent BMI, harms, and study quality using the Cochrane and Newcastle-Ottawa risk of bias tools. RESULTS: A random effects model was used to pool the effect size across eligible 10 randomized controlled trials and 2 quasi-experimental studies. Compared with usual care or control treatment, brief interventions feasible for primary care were associated with a significant but small reduction in BMI z score (-0.04, [95% confidence interval, -0.08 to -0.01]; P = .02) and a nonsignificant effect on body satisfaction (standardized mean difference 0.00, [95% confidence interval, -0.21 to 0.22]; P = .98). LIMITATIONS: Studies had methodological limitations, follow-up was brief, and adverse effects were not commonly measured. CONCLUSIONS: BMI surveillance and counseling has a marginal effect on BMI, highlighting the need for revised practice guidelines and the development of novel approaches for providers to address this problem.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.055 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".