Fracture risk assessment after BMD examination: whose job is it, anyway?
Bibliographic record
Abstract
UNLABELLED: Fracture risk assessments on bone mineral density reports guide family physicians' treatment decisions but are subject to inaccuracy. Qualitative analysis of interviews with 22 family physicians illustrates their pervasive questioning of reported assessment accuracy and independent assumption of responsibility for assessment. Assumption of responsibility is common despite duplicating specialists' work. INTRODUCTION: Fracture risk is the basis for recommendations of treatment for osteoporosis, but assessments on bone mineral density (BMD) reports are subject to known inaccuracies. This creates a complex situation for referring physicians, who must rely on assessments to inform treatment decisions. This study was designed to broadly understand physicians' current experiences with and preferences for BMD reporting; the present analysis focuses on their interpretation and use of the fracture risk assessments on reports, specifically METHODS: A qualitative, thematic analysis of one-on-one interviews with 22 family physicians in Ontario, Canada was performed. RESULTS: The first major theme identified in interview data reflects questioning by family physicians of reported fracture risk assessments' accuracy. Several major subthemes related to this included questioning of: 1) accuracy in raw bone mineral density measures (e.g., g/cm(2)); 2) accurate inclusion of modifying risk factors; and 3) the fracture risk assessment methodology employed. A second major theme identified was family physicians' independent assumption of responsibility for risk assessment and its interpretation. Many participants reported that they computed risk assessments in their practice to ensure accuracy, even when provided with assessments on reports. CONCLUSIONS: Results indicate family physicians question accuracy of risk assessments on BMD reports and often assume responsibility both for revising and relating assessments to treatment recommendations. This assumption of responsibility is common despite the fact that it may duplicate the efforts of reading physicians. Better capture of risk information on BMD referrals, quality control standards for images and standardization of risk reporting may help attenuate some inefficiency.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".