A comparison of the content and primary literature support for online medication information provided by Lexicomp and Wikipedia
Bibliographic record
Abstract
OBJECTIVES: The research compared the comprehensiveness and accuracy of two online resources that provide drug information: Lexicomp and Wikipedia. METHODS: Medication information on five commonly prescribed medications was identified and comparisons were made between resources and the relevant literature. An initial content comparison of the following three categories of medication information was performed: dose and instructions, uses, and adverse effects or warnings. The content comparison included sixteen points of comparison for each of the five investigated medications, totaling eighty content comparisons. For each of the medications, adverse reactions that appeared in only one of the resources were identified. When primary, peer-reviewed literature was not referenced supporting the discrepant adverse reactions, a literature search was performed to determine whether or not evidence existed to support the listed claims. RESULTS: Lexicomp consistently provided more medication information, with information provided in 95.0% (76/80) of the content, compared to Wikipedia's 42.5% (34/80). Lexicomp and Wikipedia had information present in 91.4% (32/35) and 20.0% (7/35) of dosing and instructions content, respectively. Adverse effects or warning content was provided in 97.5% (39/40) of Lexicomp content and 55.0% (22/40) of Wikipedia content. The "uses" category was present in both Lexicomp and Wikipedia for the 5 medications considered. Of adverse reactions listed solely in Lexicomp, 191/302 (63.2%) were supported by primary, peer-reviewed literature in contrast to 7/7 (100.0%) of adverse reactions listed only in Wikipedia. A review of US Food and Drug Administration Prescribing Information and the Adverse Event Reporting System dashboard found support for a respective 17/102 (16.7%) and 92/102 (90.2%) of Lexicomp's adverse reactions that were not supported in the literature. CONCLUSION: Lexicomp is a comprehensive medication information tool that contains lists of adverse reactions that are not entirely supported by primary-peer reviewed literature.
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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 | 0.020 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".