What Do We Know about Individuals Who Are Assessed as Being at Moderate Risk for Future Fracture in Canada?
Bibliographic record
Abstract
OBJECTIVE: We examined what was known about individuals in Canada who were assessed as being at moderate risk for future fracture. METHODS: A scoping review was conducted. Eligible articles were Canadian studies published from 2010 onwards reporting on primary data that included patients at moderate risk for future fracture. We limited the search to Canada as fracture risk categorization is unique to each country. Studies were identified by searching relevant databases. Two reviewers independently reviewed titles and abstracts to determine each study's eligibility. General information about each study, demographic information about the moderate risk groups (including tool used to determine moderate risk (Fracture Risk Assessment Tool (FRAX), Canadian Association of Radiologists and Osteoporosis Canada (CAROC)), and outcomes (number of patients: recommended treatment, prescribed treatment, initiating treatment, persisting with treatment after six months, who re-fractured, who died) were documented. RESULTS: We identified 1193 papers which were further screened for eligibility. Of the 1193 identified, 7 were eligible for the review but only 4 articles contained demographic or outcome data on moderate risk patients. In one study, 1.8% of moderate risk patients died over a mean 5.3 years of observation and in three studies, the risk of fracture was 5.9% over a median of 3 years of follow-up, 8.3% over a mean of 5.4 years, and 14.7% over 10 years of follow-up. CONCLUSION: There is a wide knowledge gap in the literature concerning individuals who are assessed as moderate risk for future fracture in Canada.
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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 | 0.011 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".