Data Characteristics Analysis of Adverse Drug Reaction/Event Report Form in China
Bibliographic record
Abstract
Objective To analyze the data characteristics of adverse drug reaction/event report form in China,and give some suggestions in building signal detection and early-warning systems from the perspective of data. Methods Collecting the adverse drug reaction/event report form of United States,Australia and Canada,and comparing with China's,then descriptive analyzing the critical data item of report form in China. Results The data item of adverse drug reaction/event report form can fully reflect the adverse drug reactions/events in China; Part of the data need ruling out and amending because of the inherent drawbacks of the design of the data item of the report form'; The report form' data item can constitute many kinds of signal detection indicators. Conclusions Must build up an effective database of signal detection and early-warning systems; The data of the report form' are rich of information,can design many kinds of detection and early-warning functions.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".