Probability of multiple fractures in childhood
Bibliographic record
Abstract
Background Fractures are common in childhood and yet little data exists on the probability of experiencing multiple fractures during childhood. Such data are important because multiple childhood fractures may indicate a correctable condition in the host child. An example of such a condition would be compromised bone health, due to genetic potential and/or vitamin D insufficiency in the diet. This would have important implications for prevention. Our objective was to empirically determine fracture risk and the cumulative risk of multiple fractures by year of age during childhood. Methods We obtained population based age incidence counts of all fractures from the Ambulatory Care Reporting System in the province of Ontario, Canada for boys and for girls from ages 0 through 15. We used the counts to estimate age specific fracture probabilities. Using combinatoric methods we calculated the probabilities of having zero, one, two, or three or more fractures at each age between 0 and 15. Results By the age of 15, 72.6% of children should have no fractures; 23.5% one fracture, 3.5% two fractures and 0.34% should have three or more fractures. Age specific probabilities for all years are in the paper. Conclusion If fractures were distributed at random in the population, very few children (under 0.34%) would be expected to have three or more fractures. Three fractures in childhood, or two before the age of 6, may be a threshold at which host factors can be sought in the child or family to inform the prevention of future fractures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".