Quality of fracture risk assessment in post-fracture care in Ontario, Canada
Bibliographic record
Abstract
UNLABELLED: As fracture risk assessment is a basis for treatment decisions, accurate risk assessments on bone mineral density (BMD) reports are important. Over 50 % of sampled BMD reports for Ontarians with fracture histories underestimated fracture risk by a single category. Risk assessments in Ontario may not accurately inform treatment recommendations. INTRODUCTION: The shifting emphasis on fracture risk assessment as a basis for treatment recommendations highlights the importance of ensuring that accurate fracture risk assessments are present on reading specialists' BMD reports. This study seeks to determine the accuracy of fracture risk assessments on a sample of BMD reports from 2008 for individuals with a history of fracture and produced by a broad cross section of Ontario's imaging laboratories. METHODS: Forty-eight BMD reports for individuals with documented history of fragility fracture were collected as part of a cluster randomized trial. To compute fracture risk, risk factors, and BMD T-scores from reports were abstracted using a standardized template and compared to the assessments on the reports. Cohen's kappa was used to score agreement between the research team and the reading specialists. RESULTS: The weighted kappa was 0.21, indicating agreement to be at the margin of "poor to fair." More than 50 % of the time, reported fracture risks did not reflect fracture history and were therefore underestimated by a single category. Over 30 % of the reports containing a "low" fracture risk assessment were assessed as "moderate" fracture risk by the research team, given fracture history. Over 20 % of the reports with a "moderate" fracture risk were assessed as "high" by the research team, given fracture history. CONCLUSIONS: This study highlights the high prevalence of fracture risk assessments that are underestimated. This has implications in terms of fracture risk categorization that can negatively affect subsequent follow-up care and treatment recommendations.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".