Abstract TP160: Predicting Bone Fracture After Stroke—The Fracture Risk After Ischemic Stroke (FRAC-Stroke) Score
Bibliographic record
Abstract
Background: Risk for low trauma fracture is increased by >30% after ischemic stroke. Additionally, in the IRIS trial pioglitazone therapy prevented ischemic stroke but increased fracture risk. We derived a risk score to predict risk of fracture one year after ischemic stroke. Methods: The Fracture Risk after Ischemic Stroke (FRAC-Stroke) Score was derived in 20,435 ischemic stroke patients from the Ontario Stroke Registry discharged from 2003-2012, using Fine-Gray competing risk regression. Candidate variables were medical conditions included in the validated World Health Organization FRAX risk score complemented by variables related to stroke severity. Registry patients were linked to population-based Ontario health administrative data to identify low trauma fractures (defined as any fracture of the femur, forearm, humerus, pelvis or vertebrae, excluding fractures resulting from trauma, motor vehicle accidents, falls from a height or in people with active cancer). The score was externally validated in 13,698 other ischemic stroke patients in the population-based Ontario stroke audit (2002-2012). Results: Mean age was 72; 42% were women. Low trauma fracture occurred within 1 year of discharge in 741/20435 (3.6%); cumulative incidence increased linearly throughout follow-up. Age, discharge modified Rankin score (mRS), and history of arthritis, osteoporosis, falls and previous fracture contributed significantly to the model. Model discrimination was good (c statistic 0.72). Including discharge mRS significantly improved discrimination (relative integrated discrimination index 8.7%). Fracture risk was highest in patients with mRS 3 and 4 but lowest in bedbound patients (mRS 5). From the lowest to the highest FRAC-Stroke quintile the cumulative incidence of 1-year low trauma fracture increased from 1% to 9%. Predicted and observed rates of fracture were similar in the external validation cohort. Conclusion: The FRAC-Stroke score allows the clinician to identify ischemic stroke patients at higher risk of low trauma fracture within one year. This information might be used to target patients for early bone densitometry screening to diagnose and manage osteoporosis, and to estimate baseline risk prior to starting pioglitazone therapy.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".