The Effects of Noncompliance to Prolia (Denosumab) on the Changes in Bone Mineral Density: A Retrospective Review
Bibliographic record
Abstract
Although denosumab (Prolia) has been shown to be a safe and efficacious therapy for osteoporotic patients in numerous clinical trials, few studies have determined its effectiveness in real world clinical practice. A retrospective review of patients prescribed Prolia assessing the impact that noncompliance from the regular dosing regimen of six months for denosumab has on bone mineral density (BMD) was performed. 924 patient records were reviewed between August 2012 and September 2013 with 436 patients meeting the eligibility criteria. Patients were divided into three groups: subsequent injection of denosumab (1) less than five months, (2) between five and seven months, and (3) more than seven months after their initial subcutaneous injection. A multivariable regression analysis was conducted comparing the differences among the three prespecified groups in BMD change (g/cm(2)) after one year of denosumab therapy at both the lumbar spine (LS) and femoral neck (FN). The differences in LS and FN BMD have shown that the relationship between the timing of drug administration in these three groups and change in BMD over 1 year was not clinically or statistically significant (p > 0.05). A follow-up study with a larger sample size and longer follow-up duration is required to further characterize this relationship.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".