Patterns of use of the bone mineral density test in Ontario, 1992-1998.
Bibliographic record
Abstract
BACKGROUND: There is ongoing controversy about who should be referred for bone mineral density (BMD) testing to estimate fracture risk and diagnose osteoporosis. The purpose of this study was to examine patterns of use of BMD testing in Ontario between 1992 and 1998. METHODS: All physician claims from the Ontario Health Insurance Plan (OHIP) claims database for BMD testing between Jan. 1, 1992, and Dec. 31, 1998, were categorized by age and sex of the patient and the specialty of the physician who ordered the test. Time trends and regional rate variation analyses were also performed. To examine the prevalence of repeat testing, an inception cohort of women who had a BMD test in 1996 was followed for 2 years from the date of first test. RESULTS: From 1992 to 1998 the number of BMD tests performed per year in women increased from 34,402 to 230,936 and in men from 2,162 to 13,579. In 1998 most tests were being ordered by family physicians (80.2% in 1998 v. 52.1% in 1992). Approximately 1 in 7 women aged 55-69 years had BMD tests done in 1998. Within a 2-year period 29.3% of these women had the test repeated; the mean time between tests was 16 months. Regional rate variation analyses of BMD tests performed in 1996-1998 indicated a 235-fold variation in BMD test rates across counties in Ontario, with a range from 0.2 to 47.1 per 1000 women in the population. INTERPRETATION: The number of BMD tests performed each year in Ontario is increasing rapidly. However, the significant variation between rates of testing in different regions indicates that the diffusion of this technology may not be taking place according to population need.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".