Diffusion of Methodological Innovation in Pharmacoepidemiology: Self-controlled Study Designs
Bibliographic record
Abstract
Self-controlled designs are methodological innovations that complement traditional observational studies and are useful to control for time-invariant confounders. The use and diffusion of self-controlled case-control and cohort designs in pharmacoepidemiology was examined over time, and described using Rogers' Diffusion of Innovations Theory and co-authorship network analysis (visualized in a supplementary graphics interchange format (GIF) image). Studies experienced a lag in diffusion, followed by a rapid uptake in use since 2000. Overall, the co-authorship network was comprised of 176 papers, 763 authors and 46 components; 31 components contained one paper (61% self-controlled case-control). The largest component of the network was interconnected and was comprised of 69% self-controlled cohort studies. Future work to develop and disseminate standardized language could target seminal authors and key opinion leaders identified in the network. Formal reporting guidelines are also encouraged, as the majority of applications did not follow recommendations on reporting, such as raw data display.
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 | 0.383 | 0.541 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".