Independent external validation of nomograms for predicting risk of low-trauma fracture and hip fracture
Bibliographic record
Abstract
BACKGROUND: A set of nomograms based on the Dubbo Osteoporosis Epidemiology Study predicts the five- and ten-year absolute risk of fracture using age, bone mineral density and history of falls and low-trauma fracture. We assessed the discrimination and calibration of these nomograms among participants in the Canadian Multicentre Osteoporosis Study. METHODS: We included participants aged 55-95 years for whom bone mineral density measurement data and at least one year of follow-up data were available. Self-reported incident fractures were identified by yearly postal questionnaire or interview (years 3, 5 and 10). We included low-trauma fractures before year 10, except those of the skull, face, hands, ankles and feet. We used a Cox proportional hazards model. RESULTS: Among 4152 women, there were 583 fractures, with a mean follow-up time of 8.6 years. Among 1606 men, there were 116 fractures, with a mean follow-up time of 8.3 years. Increasing age, lower bone mineral density, prior fracture and prior falls were associated with increased risk of fracture. For low-trauma fractures, the concordance between predicted risk and fracture events (Harrell C) was 0.69 among women and 0.70 among men. For hip fractures, the concordance was 0.80 among women and 0.85 among men. The observed fracture risk was similar to the predicted risk in all quintiles of risk except the highest quintile of women, where it was lower. The net reclassification index (19.2%, 95% confidence interval [CI] 6.3% to 32.2%), favours the Dubbo nomogram over the current Canadian guidelines for men. INTERPRETATION: The published nomograms provide good fracture-risk discrimination in a representative sample of the Canadian population.
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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 | 0.056 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".