Bibliographic record
Abstract
The goal of osteoporosis treatment is fracture risk reduction. Identification of patients at high risk for fracture is facilitated by the World Health Organization (WHO) diagnostic classification system, whereby osteoporosis is diagnosed in the presence of a T‐score of −2.5 or less. However, many fragility fractures occur in individuals with a bone mineral density (BMD) T‐score that is better than −2.5.1 As such, limiting treatment to patients with a T‐score of −2.5 or less would miss a large number of patients who will later sustain a fracture that might have been prevented by appropriate identification and early intervention. BMD combined with clinical risk factors for fracture provides a better prediction of fracture risk than BMD or clinical risk factors alone. The WHO fracture risk assessment tool, FRAX, is a computer‐based algorithm with an input of patient demographics, a “yes” or “no” response indicating the presence or absence of each of seven clinical risk factors for fracture, and femoral neck BMD (when available) to estimate the 10‐year probability of major osteoporotic fracture (ie, hip, spine, proximal humerus, and distal forearm) and the 10‐year probability of hip fracture.2 Country‐specific mortality data can be used to calibrate the fracture probabilities, with 32 countries included in FRAX at the time of this writing. Cost‐utility analysis then may be applied to FRAX‐derived data, using numerous economic, social, and political assumptions, to determine the magnitude of fracture risk at which it is likely to be cost‐effective to initiate pharmacologic therapy to reduce fracture risk. FRAX has been incorporated recently into bone densitometer software and some handheld computer devices, allowing greater access to this tool as an aid in making treatment decisions.
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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 | 0.021 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".