Can current electronic systems meet drug safety and effectiveness requirements?
Bibliographic record
Abstract
BACKGROUND: Every health policy jurisdiction is endeavoring to enhance its ability to evaluate drug effectiveness, safety and cost in the real world (pharmacosurveillance). METHODS: A nominal group consensus conference of stakeholders finalized data items deemed necessary for pharmacosurveillance. Large administrative datasets (LADs), electronic health records (EHRs) and electronic patient registries (PRs), were investigated as sources of this information and for their vulnerability to methodologic bias. Health data privacy legislation and research guidelines were systematically reviewed for their constraint to linked data resource analyses. RESULTS: More than 129 data items were strongly recommended for routine pharmacosurveillance. LADs had very complete information, but restricted to a small number of required data items. EHRs, especially with e-pharmacy links, offer by far the most complete set of health information domains but data entry completeness is highly variable. Adjustment methods for channeling bias are inadequate to mimic randomized trials. Anonymized, linked data held within a secure academic research environment, poses the least privacy concerns. CONCLUSIONS: Notwithstanding major technical, methodologic and privacy challenges, individual-level linkage of health data resources poses the best option for pharmacosurveillance today. In future, drug regulators and reimbursement agencies should consider mandatory post-marketing randomized trials.
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 | 0.199 | 0.507 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".