Why Is the Age-Standardized Incidence of Low-Trauma Fractures Rising in Many Elderly Populations?
Bibliographic record
Abstract
Low-trauma fractures of elderly people are a major public health burden worldwide, and as the number and mean age of older adults in the population continue to increase, the number of fractures is also likely to increase. Epidemiologically, however, an additional concern is that, for unknown reasons, the age-standardized incidence (average individual risk) of fracture has also risen in many populations during the recent decades. Possible reasons for this rise include a birth cohort effect, deterioration in the average bone strength by time, and increased average risk of (serious) falls. Literature provides evidence that the rise is not due to a birth cohort effect, whereas no study shows whether bone fragility has increased during this relatively short period of time. This osteoporosis hypothesis could, however, be tested if researchers would now repeat the population measurements of bone mass and density that were made in the late 1980s and the 1990s. If such studies proved that women's and men's age-standardized mean values of bone mass and density have declined over time, the osteoporosis hypothesis would receive scientific support. The third explanation is based on the hypothesis that the number and/or severity of falls has risen in elderly populations during the recent decades. Although no study has directly tested this hypothesis, a great deal of indirect epidemiologic evidence supports this contention. For example, the age-standardized incidence of fall-induced severe head injuries, bruises and contusions, and joint distortions and dislocations has increased among elderly people similarly to the low-trauma fractures. The fall hypothesis could also be tested in the coming years because the 1990s saw many research teams reporting age- and sex-specific incidences of falling for elderly populations, and the same could be done now to provide data comparing the current incidence rates of falls with the earlier ones.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".