Effectiveness of Teriparatide in Postmenopausal Women with Osteoporosis and Glucocorticoid Use: 3-Year Results from the EFOS Study
Bibliographic record
Abstract
OBJECTIVE: To describe clinical fracture rates, back pain, and health-related quality of life (HRQOL) in postmenopausal women with osteoporosis who are receiving glucocorticoids (GC), during a 36-month study of teriparatide treatment for up to 18 months, with an additional 18-month followup period when patients were receiving other osteoporosis medications. METHODS: A prospective, multinational, observational study. Data for clinical fractures, back pain (by visual analog scale; VAS) and HRQOL (by EQ-5D) were collected over 36 months. Fracture data were summarized in 6-month segments and analyzed using logistic regression with repeated measures. Changes from baseline in back pain VAS and EQ-VAS were analyzed. RESULTS: Of 1581 enrolled women with followup data, 294 (18.6%) had antecedents of GC use. Of these, 49 (16.7%) patients sustained a total of 69 fractures during the 36-month study period. Adjusted odds of fracture were significantly decreased during the last year of followup compared with the first 6 months of teriparatide treatment: an 81% decrease in the 24 to < 30-month period (p < 0.05), and an 89% decrease in the 30 to < 36-month period (p < 0.05). There were significant reductions in back pain and improvements in HRQOL in both groups of GC users and nonusers. CONCLUSION: Postmenopausal women with severe osteoporosis receiving GC, who were treated with teriparatide for up to 18 months, showed a reduced incidence of clinical fractures during the third year while receiving sequential osteoporosis treatments compared with the first 6 months, together with reduced back pain and improved HRQOL. Our results should be interpreted in the context of an uncontrolled observational study in a routine clinical setting.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".