Bibliographic record
Abstract
Abone mineral density test using dual-energy x-rayabsorptiometry (DXA) evaluates the quantity of bonemineral and is used to make a diagnosis of reduced bone mass or osteoporosis and to provide information that contributes to an assessment of fracture risk. Together with other risk factors for fracture, information gained from a bone mineral density test can guide clinicians and patients in understanding the risk of an osteoporosis-related fracture; it can also inform decisions aimed at mitigating these risks (e.g., initiation of bisphosphonates treatment).1,2 Clinical practice guidelines in Canada currently recommend bone mineral density testing in at-risk populations, namely all men and women aged 65 years or more and those who have had a fragility fracture after age 40 years.2,3 The recommended management model is based on assessment of fracture risk, which is derived in part from measured bone mineral density and appears on bone mineral density reports for most patients over age 50. For patients assessed as high risk, guidelines indi-cate that there is good evidence to support pharmacotherapy; for those assessed as low risk, guidelines state that patients are unlikely to benefit from pharmacotherapy and should be reassessed in 5 years.3 Thus, bone mineral density testing, as well as knowledge of clinical risk factors that can modify the assessment of fracture risk, are important components of pre-vention efforts to reduce (secondary) fracture risk.1,3 In the province of Ontario, Canada, a sharp increase in the rate of bone mineral density testing among women aged 40– 44 years, for whom fracture risk is typically low, was observed Impact of a change in physician reimbursement on bone mineral density testing in Ontario, Canada: a population-based study
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.717 | 0.394 |
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".