BMC Musculoskeletal Disorders BioMed Central Research article
Bibliographic record
Abstract
© 2004 Papaioannou et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. osteoporosisfractureCanadadiagnosistreatment Background: The presence of a fragility fracture is a major risk factor for osteoporosis, and should be an indicator for osteoporosis diagnosis and therapy. However, the extent to which patients who fracture are assessed and treated for osteoporosis is not clear. Methods: We performed a review of the literature to identify the practice patterns in the diagnosis and treatment of osteoporosis in adults over the age of 40 who experience a fragility fracture in Canada. Searches were performed in MEDLINE (1966 to January 2, 2003) and CINAHL (1982 to February 1, 2003) databases. Results: There is evidence of a care gap between the occurrence of a fragility fracture and the diagnosis and treatment of osteoporosis in Canada. The proportion of individuals with a fragility fracture who received an osteoporosis diagnostic test or physician diagnosis ranged from 1.7 % to 50%. Therapies such as hormone replacement therapy, bisphosphonates or calcitonin were being prescribed to 5.2 % to 37.5 % of patients. Calcium and vitamin D supplement intake was variable, and ranged between 2.8 % to 61.6 % of patients. Conclusion: Many Canadians who experience fragility fracture are not receiving osteoporosis management for the prevention of future fractures.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.345 | 0.123 |
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".