[Future Perspective of Pharmacoepidemiology in the "Big Data Era" and the Growth of Information Sources].
Bibliographic record
Abstract
The arrival of new drug into the market requires many years of previous research along with the need of continuous evaluation throughout the lifetime of the drug. This warrants pharmacoepidemiological research which may be defined as the study of the use and the effects of drugs in large populations. Nowadays this type of research seems more feasible thanks to the massive expansion of the information sources and data (e.g: clinical patient registries, electronic medical records). However there is a risk of information overload, fragmented evidence and given the enthusiasm aroused by the "Big Data", it must be emphasized that its nature is mainly observational, and therefore subject to bias and confusion. The application of epidemiological methods in this scenario seems essential for any analysis. In short, the management and use of these data sources to generate useful information expansion is the next challenge for the application of research methods in modern pharmacoepidemiology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".