Bibliographic record
Abstract
Introduction Osteoporosis is becoming one of the most common chronic diseases affecting millions of people worldwide, primarily due to the aging of the world's population. Since bone fractures are closely related to diminished bone mass and reduced bone mineral density, we have to identify all underlying causes responsible for inadequate accumulation of bone tissue during skeletal growth and consolidation, and excessive losses thereafter. Maximizing bone mass during skeletal growth, therefore, has been the goal of the primary prevention of osteoporosis, while the reduction of bone loss during menopause and aging is the problem in secondary prevention programs. Until recently, the concern for patients with osteoporosis dictated a simple approach to preventive medicine, that is to reduce the number of women suffering from it. They were considered the minority, while the majority of the population without fractures was considered normal. This approach was primarily based on the X-ray diagnosis which assumed that women fall into just two categories, namely those who have the disease and those who do not (Matkovic et al., 1995b). That there is no clear distinction between the bone health and osteoporosis was originally proposed by Newton-John and Morgan (1970) and shown for the first time in a study of fracture rates among two populations with different peak bone mass (Matkovic et al., 1979).
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".