Bone Mineral Density Reporting Underestimates Fracture Risk in Ontario
Bibliographic record
Abstract
OBJECTIVE: Analysis of clinical documents such as bone mineral density (BMD) reports is an important component of program evaluation because it can provide insights into the accuracy of assessment of fracture risk communicated to patients and practitioners. Our objective was to compare fracture risk calculations from BMD test reports to those based on the 2010 Canadian guidelines. METHODS: We retrieved BMD reports from fragility fracture patients screened through a community hospital fracture clinic participating in Ontario's Fracture Clinic Screening Program. Fracture risk was determined according to the 2010 Canadian guidelines using age, sex, and T-score at the femoral neck, in addition to three clinical factors. Three researchers classified patients' fracture risk until consensus was achieved. RESULTS: We retrieved reports for 17 patients from nine different BMD clinics in the Greater Toronto Area. Each patient had a different primary care physician and all BMD tests were conducted after the 2010 Canadian guidelines were published. The fracture risk of 10 patients was misclassified with 9 of the 10 reports underestimating fracture risk. Nine reports acknowledged that the prevalence of a fragility fracture raised the risk category by one level but only four of these reports acknowledged that the patient had, or may have sustained, a fragility fracture. When we raised fracture risk by one level according to these reports, eight patients were still misclassified. Fracture risk in the majority of these patients remained underestimated. Inconsistent classification was found in the majority of cases where reports came from the same clinic. Four reports described risk levels for two different types of risk. CONCLUSIONS: More than half of patients received BMD reports which underestimated fracture risk. Bone health management recommendations based on falsely low fracture risk are likely to be sub-optimal.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".