Lexicomp Provides More Comprehensive Drug Information than Wikipedia in Small Sample Comparison
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».