Secondary Osteoporosis in Men and Women: Clinical Challenge of an Unresolved Issue
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical and etiological factors of osteoporosis. We also tested the FRAX algorithm to compare the assessment of fracture risk in patients with primary or secondary osteoporosis. METHODS: A prospective study carried out in a large sample of 123 men and 246 women. All subjects had a biochemical, densitometric, and radiological examination of thoracic and lumbar spine. RESULTS: The prevalence of primary (men 52.9% vs women 50%; p = nonsignificant) and secondary (men 21.1% vs women 17.5%; p = nonsignificant) osteoporosis did not differ between the sexes. In contrast, the prevalence of primary osteoporosis was significantly higher than secondary causes (p < 0.0001) in both men and women. While women came to our attention for prevention of osteoporosis, men sought help because of clinical symptoms or disease-related complications, such as fractures. As evaluated by the FRAX tool, patients with osteopenia do not need treatment, in agreement with Italian guidelines. The estimated risk of major osteoporotic and hip fractures was significantly higher in women with secondary osteoporosis compared to men and also compared to women with primary osteoporosis. CONCLUSION: The prevalence of secondary osteoporosis in men is similar to that in women and it is less frequent than commonly reported. In patients with secondary osteoporosis, FRAX calculation may provide an estimate of a particularly high fracture risk in patients whose bone fragility is usually attributed to another disease.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".