Bibliographic record
Abstract
A 68-year-old postmenopausal woman presents in her family physician’s office to discuss the results of her recent bone mineral density test. She has a T-score of –2.2 at the femoral neck and 1.9 at the lumbar spine. She has no history of fracture and is otherwise healthy. She won-ders if her low bone density requires treatment. What is the 10-year risk of fracture? There are two models available in Canada to assess risk of fracture: the Fracture Risk Assess-ment Tool (FRAX; available at www.shef.ac.uk /FRAX) and the Canadian Association of Radi-ologists and Osteoporosis Canada tool (available at www.osteoporosis.ca).1 Recent guidelines issued by Osteoporosis Canada state that the choice of tool is a matter of personal preference and convenience.1 The World Health Organization (WHO) launched the FRAX tool in 2008 (Table 1).2 It calculates 10-year probabilities of fracture using multiple global observational databases that inte-grate clinical risk factors and bone mineral den-sity at the femoral neck. Both the risk of hip fracture and the risk of major osteoporotic frac-ture are calculated. A FRAX tool calibrated to Canadian rates of hip fracture has been available since July 2010.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.315 | 0.074 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".