Back pain in patients with severe osteoporosis on teriparatide or antiresorptives: a prospective observational study in a multiethnic population
Bibliographic record
Abstract
INTRODUCTION: We evaluated reduced back pain in a multiethnic population treated with teriparatide and/or antiresorptives in real-life clinical settings over 12 months. METHODS: This prospective observational study comprised 562 men and postmenopausal women (mean age 68.8 years) receiving either teriparatide (n = 230), antiresorptives (raloxifene or bisphosphonates; n = 322), or both (n = 10) for severe osteoporosis. The primary endpoint was the relative risk of new/worsening back pain at six months. RESULTS: At baseline, a higher proportion of teriparatide-treated than antiresorptive-treated patients had severe back pain (30.9% vs. 17.7%), extreme pain/discomfort (25.3% vs. 16.8%), extreme anxiety/depression (16.6% vs. 7.8%) and were confined to bed (10.0% vs. 5.3%). Teriparatide-treated patients had higher visual analog scale (VAS) scores for pain (5.8 ± 2.42 vs. 5.1 ± 2.58) and lower mean European Quality of Life-5 Dimensions (EQ-5D) scores (37.7 ± 29.15 vs. 45.5 ± 31.42) than antiresorptive-treated patients. The incidence of new/worsening back pain at six months for patients on teriparatide and antiresorptives was 9.8% and 10.3% (relative risk 0.99, 95% confidence interval 0.80-1.23), respectively. The incidence of severe back pain at 12 months was 1.3% and 1.6% in the teriparatide and antiresorptive treatment groups, respectively. Teriparatide-treated patients had lower mean VAS (2.71 ± 2.21 vs. 3.30 ± 2.37) and EQ‑5D (46.1 ± 33.18 vs. 55.4 ± 32.65) scores at 12 months. More teriparatide-treated patients felt better (82.7% vs. 71.0%) and were very satisfied with treatment (49.4% vs. 36.8%) compared to antiresorptive-treated patients. CONCLUSION: Patients treated with either teriparatide or antiresorptives had similar risk of new/worsening back pain at six months.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".