Changes in body mass index in Canadians over a five-year period: Results of a prospective, population-based study
Bibliographic record
Abstract
BACKGROUND: The initiation of the Canadian Multicentre Osteoporosis Study in 1996, and subsequent follow-up of the cohort 5 years later, provided longitudinal body mass index (BMI) data for a random sample of Canadians. METHODS: Height and weight were measured at baseline and 5 years and used to calculate BMI and assign one of six weight categories. Multiple imputation was used to adjust for missing weight at year 5. Data were stratified by age and gender. The proportion of participants moving between categories was generated, and multivariable linear regression was used to identify factors associated with weight change. RESULTS: Baseline data were available for 8548 participants, year 5 data for 6721, and year 5 weight was imputed for 1827 (17.6%). Mean BMI for every age and gender group exceeded healthy weight guidelines. Most remained within their BMI classification over 5 years, but when change occurred, BMI category was more likely to increase than decrease. Several sociodemographic, lifestyle and clinical characteristics were associated with change. CONCLUSION: Mean baseline BMI tended to be higher than recommended. Moreover, on average, men under age 45 and women under age 55 were gaining approximately 0.45 kilograms (one pound) per year, which leveled off with increased age and reversed in the oldest age groups. These findings underscore the need for public health efforts aimed at combating obesity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| 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".