Bibliographic record
Abstract
Bone densitometry is accepted as a useful quantitative measurement technique for assessing skeletal status (Miller et al., 1996) and predicting the risk of fragility fractures. Low bone mineral density (BMD) is the most important risk factor for fracture. BMD testing is an objective measurement supported by extensive data showing that low bone mass and future fracture risk are inversely related. Low bone mass is as valuable a predictor of fracture as high cholesterol or high blood pressure are as predictors of their respective clinical outcomes of myocardial infarction and stroke (The WHO Study Group, 1994). Historical risk factors cannot identify the individual patient with low bone mass with adequate certainty (Pouilles et al., 1991). This does not discount the importance of assessing other risk factors for fracture. In conjunction with BMD, risk factors add valuable information required for decisions related to which patients should be treated. Also, some risk factors can be modified to help reduce fracture risk (Cummings, 1996). This is particularly true in the perimenopausal population, or in patients with secondary conditions associated with bone loss. Diagnosis of osteoporosis using bone densitometry: the WHO criteria In order for bone densitometry to be utilized for the purpose of identifying asymptomatic individuals at risk for fracture, a paradigm shift in the definition of osteoporosis had to occur.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.038 |
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".