A North American perspective of content and quality of websites in the English language on childhood-onset lupus erythematosus
Bibliographic record
Abstract
Objective The objective of this article is to examine the quality, content, and readability of information and resources in the English language and accessible on the internet by pediatric patients with systemic lupus erythematosus (SLE) and their families in North America. Methods Keywords relevant to SLE were generated by an undergraduate student, a first-year medical student, and a third-year pediatric resident, and a search was conducted across five commonly used search engines. Quality of information found was evaluated independently by an undergraduate student, a graduate student, a first-year medical student, and a third-year pediatric resident using the DISCERN tool. Two pediatric rheumatologists assessed website accuracy and completeness. Readability of websites was determined using the Flesch-Kincaid grade level and Reading Ease score. Results Out of 2000 websites generated in the search, only 34 unique websites met inclusion criteria. Only 16 of these websites had DISCERN scores above 50% (fair quality). Overall quality of website information was fair with mean ±standard deviation (SD) DISCERN quality score of 44 ± 7 (range: 30-56). Only nine websites of 34 had DISCERN scores above 50 (>66%, indicating greater quality) and were further assessed for completeness. Flesch-Kincaid grade level was 11 ± 1 (mean±SD) and reading ease score was 39 ± 10 (mean±SD, range of 11-61). Conclusion Our study highlights the need for more complete, readable information regarding the unique needs of pediatric patients with childhood-onset SLE and their families.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".