Increase in Fracture Risk Following Unintentional Weight Loss in Postmenopausal Women: The Global Longitudinal Study of Osteoporosis in Women
Bibliographic record
Abstract
Increased fracture risk has been associated with weight loss in postmenopausal women, but the time course over which this occurs has not been established. The aim of this study was to examine the effects of unintentional weight loss of ≥10 lb (4.5 kg) in postmenopausal women on fracture risk at multiple sites up to 5 years after weight loss. Using data from the Global Longitudinal Study of Osteoporosis in Women (GLOW), we analyzed the relationships between self-reported unintentional weight loss of ≥10 lb at baseline, year 2, or year 3 and incident clinical fracture in the years after weight loss. Complete data were available in 40,179 women (mean age ± SD 68 ± 8.3 years). Five-year cumulative fracture rate was estimated using the Kaplan-Meier method, and adjusted hazard ratios for weight loss as a time-varying covariate were calculated from Cox multiple regression models. Unintentional weight loss at baseline was associated with a significantly increased risk of fracture of the clavicle, wrist, spine, rib, hip, and pelvis for up to 5 years after weight loss. Adjusted hazard ratios showed a significant association between unintentional weight loss and fracture of the hip, spine, and clavicle within 1 year of weight loss, and these associations were still present at 5 years. These findings demonstrate increased fracture risk at several sites after unintentional weight loss in postmenopausal women. This increase is found as early as 1 year after weight loss, emphasizing the need for prompt fracture risk assessment and appropriate management to reduce fracture risk in this population. © 2016 American Society for Bone and Mineral Research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".