Modern Digital Query Analytics of Patient Education Materials on Acanthosis Nigricans: Systematic Search and Content Analysis
Bibliographic record
Abstract
Background: Online digital materials are integral to patient education and health care outcomes in dermatology. Acanthosis nigricans (AN) is a common condition, often associated with underlying diseases such as insulin resistance. Patients frequently search the internet for information related to this cutaneous finding. To our knowledge, the quality of online educational materials for AN has not been systematically examined. Objective: The primary objective of this study was to profile the readability and quality of the content of publicly available digital educational materials on AN and identify questions frequently asked by patients. Methods: This study analyzed publicly available internet sources to identify the most frequent questions searched by patients regarding AN using the Google Rankbrain algorithm. Furthermore, available articles on AN were evaluated for quality and reading level using metrics such as the Brief DISCERN score, and readability was determined using three specific scales including the Flesch-Kincaid score, Gunning Fog index, and the Coleman-Liau index, based on literature. Results: Patients most frequently accessed facts on AN from government sources, which comprised 30% (n=15) of the analyzed sources. The available articles did not meet quality standards and were at a reading level not appropriate for the general public. The majority of articles (n=29/50, 58%) had substandard Brief DISCERN scores, failing to meet the criteria for good quality. Conclusions: Clinicians should be aware of the paucity of valuable online educational material on AN and educate their patients accordingly.
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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 | 0.023 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.078 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".