Bibliographic record
Abstract
Osteoporosis is one of the leading causes of morbidity and mortality amongst the elderly. With the prediction that the number of people who are 60 years or more will increase from ∼300 million to greater than 700 million in the next 25 years, it can be appreciated that osteoporosis will rapidly reach epidemic proportions. This will not only represent a huge health care cost but also compromise the physical well-being and quality of life of a substantial segment of the world's population. Osteoporosis has been defined as ‘… a systemic skeletal disease characterized by low bone mass and microarchitectural deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fracture.’ By World Health Organization standards, the term osteoporosis is used to designate bone mass values of 2.5 standard deviations below the young adult mean. Using this criterion, based on bone mass alone, 18 million North Americans have established osteoporosis and 10 million have osteopenia, which is a major risk factor for osteoporosis. As a consequence of these alarming predictions there has been a steady increase in the attention focused on the physiology and pathophysiology of bone. Many excellent texts are now available, which deal in depth with topics related to the clinical presentation and management of osteoporosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.556 | 0.401 |
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".