Advancing Comparative Effectiveness Research: Filling in the Gaps for Bisphosphonates
Bibliographic record
Abstract
Drug approval regulations for market-entry often only require that medications be proven efficacious compared to placebo. This drug approval policy creates a gap in information for clinicians and policy makers to make informed decisions between treatment options. Comparative effectiveness research seeks to address these knowledge gaps, primarily through the use of administrative data and network meta-analysis (NMA). The use of bisphosphonates for the treatment of osteoporosis is representative of this problem. In Canada, there are currently four approved bisphosphonates indicated for the treatment of osteoporosis. This thesis is comprised of three unique projects contributing to comparative effectiveness research of bisphosphonate therapy. The projects address methodological gaps in the development and uses of the disease risk score (DRS), a confounder summary score, and clinical gaps in the comparative safety of bisphosphonates leveraging NMA methodology. The methodological findings of the thesis raise caution towards the standard practice of applying the DRS in situations where policy-induced bias is present, suggest the need for more consistent nomenclature for the DRS, and points to future areas for development of the DRS. The clinical findings of the thesis demonstrate little difference in serious adverse events between oral bisphosphonates. Future research should address questions related to the impact of comparative safety and adherence of bisphosphonates. Overall, this dissertation addresses important gaps in utilization and applications of the DRS, implications of policy-induced selection bias, and comparative safety and adherence of bisphosphonate therapy.
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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 | 0.593 | 0.781 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".