Fracture outcome definitions in observational osteoporosis drug effects studies: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to describe fracture outcome definitions in observational osteoporosis drug effects studies from Canada and the United States. INTRODUCTION: Health care administrative data are commonly utilized in pharmacoepidemiologic studies. These data are used to define outcomes, such as fractures, and are critical to determining real-world safety and effectiveness of medications. However, there is no current standard for fracture outcome definitions in observational studies. As a result, fractures are inconsistently defined. To inform future research, a synthesis of how fractures are defined in observational studies using health care administrative claims data is needed. Providing clarity on how fractures are defined will provide guidance for future research. INCLUSION CRITERIA: We will include observational studies from the United States and Canada that consider the impact of osteoporosis pharmacotherapies on fracture risk and leverage health care administrative data. METHODS: This review will follow the three-step JBI methodology for scoping reviews. We will search MEDLINE, Embase, and CINAHL for studies published in English from 2000 to the present. Following de-duplication, titles and abstracts will be screened independently by two reviewers. We will then conduct full-text screening for eligible studies. In addition, Canadian and US government pharmacovigilance websites will be searched to identify gray literature. Data extraction will be completed by two reviewers. Results will be presented in figures and in tabular format.
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 | 0.142 | 0.120 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.012 |
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".