Evaluating the Readability and Quality of Online Health Information Regarding Hemifacial Microsomia
Bibliographic record
Abstract
INTRODUCTION: Patients and parents increasingly rely on the internet to obtain medical information. The readability of these online webpages is significant, as lower literacy rates have been associated with poorer health outcomes. As such, the American Medical Association (AMA) and National Institutes of Health (NIH) recommend that health information be written between a 6th- and 8th-grade reading level. This study aimed to evaluate the readability and quality of online webpages discussing hemifacial microsomia (HFM). METHODS: Three of the largest online search engines were queried by 2 independent reviewers for "hemifacial microsomia." Readability was assessed using 6 readability tests: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Simple Measure of Gobbledygook (SMOG) Index, Coleman-Liau Index (CLI), and Automated Readability Index (ARI). The quality of online webpages was assessed using the DISCERN handbook and scale. RESULTS: Thirteen webpages were included for analysis. The mean overall readability level was equivalent to a 13th-grade level. The mean readability grade level for each score used was: FKGL 12.4, GFI 15.7, SMOG Index 11.3, CLI 14.1, and ARI 13.2. The FRES was 36.8 (ie, difficult to read). CONCLUSION: Online webpages providing information regarding HFM are too difficult for most Americans to read. The readability of online patient information should be a priority for health care providers and medical organizations that publish this information. By improving the readability and quality of online health information, patients and caregivers will better understand their condition, effectively encouraging active participation in the shared decision-making process.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".