Editorial: Women in pharmacoepidemiology: 2021
Bibliographic record
Abstract
This is the first Research Topic offering the opportunity to promote the work of women scientists at different stages of their careers, worldwide, and in all areas of pharmacoepidemiology.This Research Topic contains 11 studies led by women from different parts of the world, including five studies from Brazil, three from Canada, one from the United States, one from Switzerland and one from Romania.The work presented here highlights the diversity of research carried out across the breadth of pharmacoepidemiology research and presents advances in theory and methodology with applications to compelling problems.This current Research Topic includes studies related to chronic health conditions, including cardiovascular, renal, respiratory, and cancer diseases.An important theme present in this Research Topic is the use of real world data (RWD) to generate evidence for decision-making.RWD has gained significant attention in the field of research as it provides a valuable data source beyond traditional clinical trials and lab-based experiments.RWD can come from various sources, including but not limited to healthcare administrative data, hospitalization databases, electronic health databases, surveys, data from government agencies and others.Healthcare administrative data refers to the information collected and maintained by healthcare organizations, insurers, and government agencies for the purpose of managing, tracking, and reimbursing various aspects of healthcare services.An interesting scoping review (Bukhtiyarova et al.) was conducted to explore the current state of existing research according to the application of Artificial intelligence (AI) to healthcare administrative data, including those involving medications.The application of AI to healthcare administrative data is heterogeneous in terms of areas of interest and methods.One of the points highlighted by the authors is that AI can significantly improve research on the utilization of healthcare administrative data.This phenomenon can be explained by the accumulation of large volumes of this type of data, improved access to such databases for AI researchers, further development of AI methods, improved computational capacities, and increased funding of interdisciplinary projects.The authors found that many studies were focused on data from hospitals and emergency departments which can be explained by better accumulation of data by large hospitals that are often affiliated to
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.037 | 0.025 |
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".