Quality and readability of online information on idiopathic subglottic stenosis
Bibliographic record
Abstract
Abstract Objective Idiopathic subglottic stenosis (ISS) is a chronic condition characterized by disease recurrence and multiple surgeries. These frustrated patients may utilize the internet to research their condition. The aim of this study was to determine the quality and readability of online ISS information. Methods “Idiopathic subglottic stenosis” was entered into Google. The first 50 websites that met inclusion criteria were extracted. The DISCERN instrument, Flesch Reading Ease Score (FRES), and Flesch‐Kincaid Grade Level (FKGL) assessed the quality and readability, respectively. Means, SDs, Pearson correlation coefficients, and two‐tailed Student's t‐test were calculated. Results The 50 websites consisted of 17 patient‐targeted and 33 professional‐targeted websites, plus 30 major and 20 minor websites. The overall DISCERN, FRES, and FKGL scores were 2.81 ± 0.99, 27.75 ± 15.27, and 13.65 ± 2.79, respectively (mean ± SD). Patient‐targeted websites had significantly lower quality (DISCERN [P < .00]) but were easier to read (lower FKGL [P < .00], higher FRES [P < .00]) than professional‐targeted websites. Minor websites had a significantly lower quality (DISCERN [P < 0.00]) but were easier to read (lower FKGL [P < .00], higher FRES [P < .00]) than major websites. There was a positive correlation between overall quality and difficulty in readability. Conclusion The quality of online ISS information was suboptimal. Resources were too difficult to comprehend and readability scores were above AMA and NIH recommendations. Improved online information is required to properly educate this patient population. Level of Evidence Level 4.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 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".