Imputation of 10-year osteoporotic fracture rates from hip fractures: A clinical validation study
Bibliographic record
Abstract
The World Health Organization (WHO) fracture risk assessment system (FRAX) allows for calibration from country-specific fracture data. The objective of this study was to evaluate the method for imputation of osteoporotic fracture rates from hip fractures alone. A total of 38,784 women aged 47.5 years or older at the time of baseline femoral neck bone mineral density (BMD) measurement were identified in a database containing all clinical dual energy X-ray absorptiometry (DXA) results for the Province of Manitoba, Canada. Health service records were assessed for the presence of nontrauma osteoporotic fracture codes after BMD testing (431 hip, 787 forearm, 336 clinical vertebral, and 431 humerus fractures). Ten-year hip and osteoporotic fracture rates were estimated by the Kaplan-Meier method. The population was stratified by age (50 to 90 years, 5-year width strata) and again by femoral neck T-scores (-4.0 to 0.0, 0.5 SD width strata). Within each stratum, the ratio of hip to osteoporotic fractures was calculated and compared with the predicted ratio from FRAX. Increasing age was associated with greater predicted hip-to-osteoporotic ratios (youngest 0.07 versus oldest 0.41) and observed ratios (youngest 0.10 versus oldest 0.48). Lower T-scores were associated with greater predicted (highest 0.04 versus lowest 0.71) and observed ratios (highest 0.06 versus lowest 0.44). There was a strong positive correlation between predicted and observed ratios (Spearman r = 0.90-0.97, p < .001). For 14 of the 18 strata, the predicted ratio was within the observed 95% confidence interval (CI). Since collection of population-based hip fracture data is considerably easier than collection of non-hip fracture data, this study supports the current emphasis on using hip fractures as the preferred site for FRAX model calibration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".