Bibliographic record
Abstract
Although our AwAreness And understAnding of osteoporosis hAs been steadily increasing over recent years, it is still a condition that may be overlooked, particularly when our patients have other age-related morbidities such as cardiovascular diseases or cancer that elicit more attention. As we know, the effects of osteoporosis and the fragility fractures that may result can be devastating for both individuals and the health care system as a whole (see page S3). We are fortunate to have access to many new elements to assist us in the diagnosis and management of this disease — new Canadian clinical practice guidelines focused on assessing a patient’s absolute fracture risk, new biologic pathways in bone turnover discovered and explained and innovative new therapies and dosage forms developed that expand options for individualized management (see table page S10). This supplement provides primary care practitioners with information and perspective to assist them in working with patients at risk for or living with osteoporosis. I’m very grateful for the expert guidance of our Guest Editors, Drs. Robert Josse and Anne Marie Whelan, and for the contributions of all our esteemed authors. I’d like to thank Mary Elias, Dr. Heather Frame, Dr. Sarah Jennings, Dr. Brent Kvern, Marie-Claude Laliberte, Helene Perrier, Dr. Carlos Rojas-Fernandez and Dr. Susan Whiting for reviewing portions of this supplement. I’d also like to thank members of the Scientific Advisory Council of Osteoporosis Canada for their time and thoughtful additions to this project. n
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.111 | 0.042 |
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".