Symptomatic fracture incidence in those under 50 years of age in southern Tasmania
Bibliographic record
Abstract
OBJECTIVE: To document symptomatic fracture incidence in those aged under 50 years of age. METHODS: Fractures were ascertained from X-ray reports containing the word 'fracture' from all radiology providers for the geographically defined population of southern Tasmania (n = 165 175) for the period 1 July 1997 to 30 June 1999. RESULTS: In the 2-year study frame there were 2943 fractures in 164 730 person years in males and 1348 fractures in 165 620 person years in females. This represents a fracture incidence of 1787 per 100 000 person years in males and 819 per 100 000 person years in females. Peak fracture incidence was 10-14 years in females and 15-19 years in males although different fracture types had varying peak incidence suggesting different fracture-specific causes. The most common fractures were those of the hand (24%), forearm (17%), wrist (10%) and foot (9%). All fractures (including vertebral) were more common in males with relative risks ranging from 1.34 to 4.50. The estimated probability of at least one fracture between birth and 50 years of age was 59% for males and 34% for females. CONCLUSION: There are threefold as many fractures in this age group compared to those due to osteoporosis in the elderly in any given year. More research priority needs to be given to understanding the causes of these fractures so that preventive strategies can be formulated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".