Quality and Readability of Online Information on In-Office Vocal Fold Injections
Bibliographic record
Abstract
Objectives: Vocal fold injection augmentations are increasingly being performed in the office setting on awake patients, as opposed to the operating room. These procedures thus require patient cooperation and education. As the Internet is a widely-used resource for patients, our aim was to assess the quality and readability of online resources on in-office awake vocal fold injections. Methods: An online Google search using the terms “office vocal fold injection medialization” and “awake vocal fold injection” was conducted. The first 50 English-language websites were categorized into professional- and patient-targeted, and major and minor sources. They were analyzed using the Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL) test, and DISCERN quality score. Results: Fifty websites were evaluated, and the overall DISCERN score was 2.60 ± 1.01, the mean FRES was 32.16 ± 19.10, and the mean FKGL was 13.76 ± 4.12. Between the 25 professional-targeted and 25 patient-targeted websites, professional-targeted sites had significantly higher DISCERN ( P < .05) and FKGL ( P < .05) scores, and lower FRES ( P < .05) scores. Between the 30 major and 20 minor websites, major websites had significantly lower FRES ( P < .05) and higher FKGL ( P < .05) scores, and there was a trend toward significance for higher DISCERN scores ( P = .052). Conclusions: Our study shows that half of the top Google results for our topic were not written for patient education, but rather for health care professionals. The reading level of this information exceeds the recommended grade level for patient education materials, and may be less comprehensible than intended. While patient-targeted materials are easier to read than professional-targeted sites, they are of lower quality. The quality of the available online information on this topic is suboptimal for both patients and health care providers. This research highlights the need for more appropriate patient education materials given low health literacy rates.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".