Readability, Quality, and Timeliness of Patient Online Health Resources for Contact Dermatitis and Patch Testing
Bibliographic record
Abstract
BACKGROUND: Contact dermatitis (CD) causes significant impact on patient quality of life. It is the most common occupational skin disease, accounting for more than US $1 billion of medical costs. Patch testing (PT) is the criterion standard for diagnosis of allergic CD. Patients are increasingly using the Internet to obtain health information; however, the readability, quality, and timeliness of online health resources for CD and PT are unknown. OBJECTIVE: The objective was to determine the readability, quality, and timeliness of the most frequently accessed patient online health resources for CD and PT. METHODS: A Google search was performed on March 20, 2021, using the terms "contact dermatitis," "contact eczema," "patch testing," and "patch test." Websites were evaluated using several well-validated tools/criteria. RESULTS: Contact dermatitis and PT websites had only 2 of the 48 websites combined that met the recommended sixth-grade reading level for patients, with the majority characterized as "very poor" to "fair" quality. There was no correlation found between quality and readability of the CD and PT websites. CONCLUSIONS: There is a need for improvement of online CD and PT health resources. Dermatologists should take the lead to vet websites and enhance online resources to improve patient care.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".