Low Osteoporosis Treatment Initiation Rate in Women after Distal Forearm or Proximal Humerus Fracture: A Healthcare Database Nested Cohort Study
Bibliographic record
Abstract
Treatment initiation rates following fragility fractures have often been reported to be low and in recent years numerous programs have been implemented worldwide to increase them. This study aimed at describing osteoporosis (OP) treatment initiation in a representative sample of women who were hospitalized for a distal forearm fracture (DFF) or proximal humerus fracture (PHF) in 2009-2011 in France. The data source was a nationwide sample of 600,000 individuals, extracted from the French National Insurance Healthcare System database. All women aged 50 years and older who were hospitalized for a DFF or PHF between 2009 and 2011 and who had not received any OP treatment in the preceding 12 months were included in a retrospective cohort study. OP treatments initiated during the year following the fracture were analyzed. From 2009 to 2011, 729 women were hospitalized for a DFF or a PHF and 284 were on OP treatment at the time of the fracture occurrence. Among the 445 women who had no prevalent OP treatment, 131 (29.4%) received supplementation treatment only (vitamin D and/or calcium) and 42 (9.4%) received a pharmacologic OP treatment in the year following their fracture. Pharmacological OP treatments included bisphosphonates (n = 21), strontium ranelate (n = 14), hormone replacement therapy (n = 4), or raloxifene (n = 3). General practitioners prescribed 75% of initial OP treatments. Despite the guidelines published in 2006 and the numerous initiatives to promote post-fracture OP treatment, OP treatment initiation rate in women who were hospitalized for a fragility fracture remained low in 2009-2011 in France.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".