Population-Wide Impact of Non-Hip Non-Vertebral Fractures on Mortality
Bibliographic record
Abstract
Data on long-term consequences of non-hip non-vertebral (NHNV) fractures, accounting for approximately two-thirds of all fragility fractures, are scanty. Our study aimed to quantify the population-wide impact of NHNV fractures on mortality. The national population-based prospective cohort study (Canadian Multicentre Osteoporosis Study) included 5526 community dwelling women and 2163 men aged 50 years or older followed from July 1995 to September 2013. Population impact number was used to quantify the average number of people for whom one death would be attributable to fracture and case impact number to quantify the number of deaths out of which one would be attributable to a fracture. There were 1370 fragility fractures followed by 296 deaths in women (mortality rate: 3.49; 95% CI, 3.11 to 3.91), and 302 fractures with 92 deaths in men (5.05; 95% CI, 4.12 to 6.20). NHNV fractures accounted for three-quarters of fractures. In women, the population-wide impact of NHNV fractures on mortality was greater than that of hip and vertebral fractures because of the greater number of NHNV fractures. Out of 800 women, one death was estimated to be attributable to a NHNV fracture, compared with one death in 2000 women attributable to hip or vertebral fracture. Similarly, out of 15 deaths in women, one was estimated to be attributable to a NHNV fracture, compared with one in over 40 deaths for hip or vertebral fracture. The impact of forearm fractures (ie, one death in 2400 women and one out of 42 deaths in women attributable to forearm fracture) was similar to that of hip, vertebral, or rib fractures. Similar, albeit not significant, results were noted for men. The study highlights the important contribution of NHNV fractures on mortality because many NHNV fracture types, except for the most distal fractures, have serious adverse consequences that affect a significant proportion of the population. © 2017 American Society for Bone and Mineral Research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".