Validity of two weight prediction models for community-living patients participating in a weight loss program
Bibliographic record
Abstract
Models predicting individual body weights over time clarify patient expectations in weight loss programs. The accuracy of two commonly used weight prediction models in community living people is unclear. All eligible people entering a weight management program between 1992 and 2015 were included. Patients' diet was 1200 kcal/day for week 0 followed by 900 kcal/day for weeks 1-7 and were excluded from the analysis if they were nonadherent. We generated expected weights using the National Institutes of Health Body Weight Planner (NIH-BWP) and the Pennington Biomedical Research Center Weight Loss Predictor (PBRC-WLP). 3703 adherent people were included (mean age 46 years, 72.6% women, mean [SD] weight 262.3 pounds [54.2], mean [SD] BMI 42.4 [7.6]). Mean (SD) relative body weight differences (100*[observed-expected]/expected) for NIH-BWP and PBRC-WLP models was - 1.5% (3.8) and - 2.9% (3.2), respectively. At week 7, mean squared error with NIH-BWP (98.8, 83%CI 89.7-108.8) was significantly lower than that with PBRC-WLP (117.7, 83%CI 112.4-123.4). Notable variation in relative weight difference were seen (for NIH-BWP, 5th-95th percentile was - 6.2%, + 3.7%; Δ 9.9%). During the first 7 weeks of a weight loss program, both weight prediction models returned expected weights that were very close to observed values with the NIH-BWP being more accurate. However, notable variability between expected and observed weights in individual patients were seen. Clinicians can monitor patients in weight loss programs by comparing their progress with these data.
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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 | 0.048 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".