Lexicomp Provides More Comprehensive Drug Information than Wikipedia in Small Sample Comparison
Bibliographic record
Abstract
A Review of:
 Hunter, J. A., Lee, T., & Persaud, N. (2018). A comparison of the content and primary literature support for online medication information provided by Lexicomp and Wikipedia. Journal of the Medical Library Association: JMLA, 106(3), 352-360. http://dx.doi.org/10.5195/jmla.2018.256
 Abstract 
 Objective – To compare the content veracity and comprehensiveness of Lexicomp and Wikipedia with respect to drug information.
 Design – Comparative study.
 Subjects – Lexicomp and Wikipedia.
 Methods – Five of the six most commonly prescribed medications in Canada were selected for content comparison in both Lexicomp and Wikipedia (levothyroxine, atorvastatin, pantoprazole, acetylsalicylic acid, and metformin). Three categories compared included dose and instructions, uses, and adverse effects or warnings; sixteen subcategories were identified to provide further comparative detail. Five outcomes were assessed using a rating scale to identify the presence or absence of each subcategory for each drug entry: present in neither source, present in Wikipedia but not Lexicomp, present in Lexicomp but not in Wikipedia, present in both without discrepancies, and present in both with discrepancies. The only subcategory meeting the criteria for “present in both with discrepancies” for all five medications was adverse reactions, indicating that the information in each resource differed. A “fact-checking literature search” in MEDLINE and EMBASE as well as searches in the USFDA Prescribing Information (supplemental index) (FDA PIs) and the FDA Adverse Events Reporting Systems (FDAERS) were used to determine the veracity of the discrepancies. Quantitative assessment was used to determine how comprehensive the entries were in terms of the number of times in which each resource provided subcategory information. Adverse reaction information was expressed as a percentage based on the number of adverse reactions identified in the sources.
 Main Results – Overall, Lexicomp was shown to provide more comprehensive information than Wikipedia. In the subheading analysis, there was no instance in which Wikipedia contained information while Lexicomp did not, while in over half of instances Lexicomp only contained the information. 18% of subheading information was found in both with discrepancies and 20% was found in both without discrepancies. Only 10% of instances were not present in Lexicomp or Wikipedia. Detailed dosing information was consistently present in Lexicomp for all five medications while only general dosage information was present in just two instances in Wikipedia.
 Of all the subcategory comparisons, adverse reactions was the only one identified as “present with discrepancies” for all medications being compared; MEDLINE, EMBASE, FDA PIs and the FAERS dashboard searches were performed for a total of 309 discrepant adverse reactions. 63% (191/302) of the adverse reactions listed in Lexicomp were supported by the literature retrieved from MEDLINE and EMBASE compared to 100% (7/7) of those listed in Wikipedia. Of the Lexicomp adverse reactions unsupported by the peer-reviewed literature, 17% were supported from information found in FDA PIs and 90% supported from information found in the FAERS dashboard. A “substantial proportion” of adverse events listed in Lexicomp were not supported in any retrieved literature.
 Conclusion – Based on the comparative criteria, drug information in Lexicomp for the five medications was found to be more comprehensive than Wikipedia. Adverse effects listed in Lexicomp did not always have corresponding support in the published peer-reviewed literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.228 |
| 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".