An Evaluation of Wikipedia as a Resource for Patient Education in Nephrology
Bibliographic record
Abstract
Wikipedia, a multilingual online encyclopedia, is a common starting point for patient medical searches. As its articles can be authored and edited by anyone worldwide, the credibility of the medical content of Wikipedia has been openly questioned. Wikipedia medical articles have also been criticized as too advanced for the general public. This study assesses the comprehensiveness, reliability, and readability of nephrology articles on Wikipedia. The International Statistical Classification of Diseases and Related problems, 10th Edition (ICD-10) diagnostic codes for nephrology (N00-N29.8) were used as a topic list to investigate the English Wikipedia database. Comprehensiveness was assessed by the proportion of ICD-10 codes that had corresponding articles. Reliability was measured by both the number of references per article and proportion of references from substantiated sources. Finally, readability was assessed using three validated indices (Flesch-Kincaid grade level, Automated readability index, and Flesch reading ease). Nephrology articles on Wikipedia were relatively comprehensive, with 70.5% of ICD-10 codes being represented. The articles were fairly reliable, with 7.1 ± 9.8 (mean ± SD) references per article, of which 59.7 ± 35.0% were substantiated references. Finally, all three readability indices determined that nephrology articles are written at a college level. Wikipedia is a comprehensive and fairly reliable medical resource for nephrology patients that is written at a college reading level. Accessibility of this information for the general public may be improved by hosting it at alternative Wikipedias targeted at a lower reading level, such as the Simple English Wikipedia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".