Estimating the incidence of vertebral deformities in Canadian men and women
Bibliographic record
Abstract
Background. Vertebral deformities are important sequelae of osteoporosis, but for feasibility and technical reasons their epidemiology has yet to be thoroughly described in Canada, especially in men. Objective. To estimate the incidence of osteoporotic vertebral deformities, from data collected by the Canadian Multicentre Osteoporosis Study (CaMos), a large cohort study of randomly selected Canadians radiographed at a five year interval. Methods. Sex- and age-specific incidence was estimated in men and women aged 55 years and older. Bayesian methods were employed, including adjustment for nonresponse and attrition biases using multiple imputation. Different assumptions for the missing data mechanism were used in a sensitivity analysis. Results. Weighted to the Canadian population, men aged 55+ have a crude incidence estimate of 17.7/1000 person-years (PY) (95% CrI: 13.5 - 22.1), whereas the corresponding estimate in women is 14.6/1000 PY (95% CrI: 12.2 - 17.1). Adjustment for bias due to attrition has only a slight effect on the estimates in women across all age groups and in men aged 65+ years, under the assumption that the missing data mechanism is ignorable. The rate estimates that are adjusted for both nonresponse and attrition biases variably diverge from the crude estimates both in magnitude and direction, depending on the assumptions made about the missing data mechanism. Conclusions. A reasonable assumption for modeling the missing data mechanism is that the sex- and age-specific biases are at least as large, and in the same direction, as the differences between the respondent rates and the imputed rates for groups with missing deformity data. Therefore, in Canadians aged 55+ years, vertebral deformity rates that are adjusted for nonresponse and attrition biases are estimated as 14.4/1000 PY (95% CrI: 11.8 - 17.4) in women, and 23.8/1000 PY (95% CrI: 19.6 - 29.0) in men.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".