Readability and quality assessment of internet-based patient education materials related to nasal septoplasty
Bibliographic record
Abstract
BACKGROUND: Given that nasal septoplasty is a common procedure in otolaryngology - head and neck surgery, the objective of this study was to evaluate the quality and readability of online patient education materials on septoplasty. METHODS: A Google search was performed using eight different search terms related to septoplasty. Six different tools were used to assess the readability of included patient education materials. These included the Flesch-Kincaid Grade Level, Flesch Reading Ease, Gunning-Fog Index, Simple Measure of Gobbledygook Index, Coleman-Liau Index, and Automated Readability Index. The DISCERN tool was used to assess quality and reliability. RESULTS: Eighty-five online patient education materials were included. The average Flesch-Reading Ease score for all patient education materials was 54.9 ± 11.5, indicating they were fairly difficult to read. The average reading grade level was 10.5 ± 2.0, which is higher than the recommended reading level for patient education materials. The mean DISCERN score was 42.9 ± 10.5 and 42% (36/85) of articles had DISCERN scores less than 39, corresponding to poor or very poor quality. CONCLUSION: The majority of online patient education materials on septoplasty are written above the recommended reading levels and have significant deficiencies in terms of their quality and reliability. Clinicians and patients should be aware of the shortcomings of these resources and consider the impact they may have on patients' decision making.
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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 | 0.009 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".