Derivation and validation of the Ottawa weight loss prediction model for patients on a low-calorie diet
Bibliographic record
Abstract
Accurate weight predictions are essential for weight management program patients. The freely available National Institutes of Health Body Weight Planner (NIH-BWP) returns expected weights over time but overestimates weight when patients consume a low-calorie diet. This study sought to increase the accuracy of NIH-BWP predicted weights for people on low-calorie diets. People enrolled in a weight management program were included if they received meal replacements with defined caloric content for the 3 months of the weight loss phase of the program. The Ottawa Weight Loss Prediction Model (OWL-PM) modelled the relative difference between observed and NIH-BWP predicted weights using longitudinal analysis methods based on patient factors. OWL-PM was externally validated. 1761 people were included (mean age 46 years, 73.3% women) with a mean (SD) baseline weight in pounds and body mass index of 271.9 (55.6) and 43.9 (7.4), respectively. At the end of the program's weight loss phase, people lost a median (IQR) of 17.1% (14.8-19.5) of their baseline weight. Observed weight relative to NIH-BWP predicted weights had a median value of - 4.9% but ranged from - 32.1% to + 28.5%. After adjustment, weight overestimation by NIH-BWP was most pronounced in male patients, people without diabetes and with increased observation time. OWL-PM returned expected weights at 3 months that were significantly more accurate than those from NIH-BWP alone (mean difference observed vs. expected [95% CI] 6.7lbs [6.4-7.0] vs. 12.6lbs [12.1-13.0]). In the external validation cohort (n = 106), OWL-PM was significantly more accurate than NIH-BWP (mean squared error 24.3 vs. 40.0, p = 0.0018). OWL-PM incorporated patient-level covariates to significantly increase weight prediction accuracy of NIH-BWP in patients consuming a low-calorie diet.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".