A critical readability and quality analysis of internet‐based patient information on neck dissections
Bibliographic record
Abstract
Objective: Patients are increasingly turning to the Internet as a source of healthcare information. Given that neck dissection is a common procedure within the field of Otolaryngology - Head and Neck Surgery, the aim of this study was to evaluate the quality and readability of online patient education materials on neck dissection. Methods: A Google search was performed using the term "neck dissection." The first 10 pages of a Google search using the term "neck dissection" were analyzed. The DISCERN instrument was used to assess quality of information. Readability was calculated using the Flesch-Reading Ease, Flesch-Kincaid Grade Level, Gunning-Fog Index, Coleman-Liau Index, and Simple Measure of Gobbledygook Index. Results: = 16) of patient education materials had Flesch-Reading Ease scores above the recommended score of 65. The average reading grade level was 10.5 ± 2.1. The average total DISCERN score was 43.6 ± 10.1. Only 26% of patient education materials (PEMs) had DISCERN scores corresponding to a "good quality" rating. There was a significant positive correlation between DISCERN scores and both Flesch-Reading Ease scores and average reading grade level. Conclusions: The majority of patient education materials were written above the recommended sixth-grade reading level and the quality of online information pertaining to neck dissections was found to be suboptimal. This research highlights the need for patient education materials regarding neck dissection that are high quality and easily understandable by patients.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| 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".