A systematic assessment of the availability and clinical drug information coverage of machine-readable clinical drug data sources for building knowledge translation products
Bibliographic record
Abstract
Objective: To identify and describe clinical drug data sources that have the potential to serve as a repository of information for developing drug knowledge translation products. Methods: Two reviewers independently screened citations from PubMed and Embase, websites from the web search engine Google, and references from selected journals. Publicly licensed or non-proprietary data sources containing clinical drug information accessible in a machine-readable format were eligible. Data sources were assessed for their coverage across 18 pre-specified domains and 74 elements of clinical drug information. Results: Of the 3369 unique citations or webpages screened, 44 drug information data sources were identified. Of these, 22 data sources met the study inclusion criteria. There was a mean of 4.5 (SD = 5.19) domains covered by each source and a mean of 10.9 (SD = 18) elements covered by each source. None of the data sources covered all domains and eight elements were not addressed by any source. All of the data sources identified by the study are government or academic databases. Conclusion: Our study demonstrated the availability of machine-readable clinical drug data that could help facilitate the creation of novel drug knowledge translation products. However, we identified clinical content gaps in the available non-proprietary drug information sources. Further evaluation of the quality of each data source would be necessary prior to incorporating these sources into any knowledge translation products intended for clinical use.
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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 | 0.166 | 0.502 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.076 | 0.041 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".