Abstract: A Comparative Readability Analysis of Online Patient Information Regarding Breast Reconstruction Following Mastectomy
Bibliographic record
Abstract
INTRODUCTION: The internet is a widely-used resource for patients who seek surgical information. Many patients have wrong expectations of treatment options due to low quality surgical information online.1 High quality patient information should not exceed a 7th-grade reading level according to the United States Department of Health and Human Services (USDHHS).2 We undertook a comparative readability assessment of patient information regarding breast reconstruction following mastectomy. METHOD: Materials were downloaded from 7 websites in January 2016: Breast Cancer Network Australia (BCNA), British Association of Plastic Reconstructive and Aesthetic Surgeons (BAPRAS), Canadian Cancer Society, Cancer Research UK, Johns Hopkins Breast Center, Mayo Clinic, National Institutes of Health (NIH). The text was processed and formatted in Microsoft Word. Specific anatomical and medical terms were excluded to limit bias. A readability assessment was undertaken on the remaining text using 6 quantitative formulas: Automated Readability Index, Coleman-Liau Index, SMOG Index, Gunning-Fog score, Flesch-Kincaid Grade level and Flesch-Kincaid Reading Ease using the Readability Studio program (Oleander Software). RESULTS: The edited and original texts had almost identical mean grade score (±0.07). Johns Hopkins Breast Center had the highest mean grade score (12.8 ± 2). Mayo Clinic had the lowest mean grade scores (10.7 ± 2). ANOVA analysis demonstrated no statistical difference between the grade scores when comparing websites (p>0.05). The mean Flesch-Kincaid Reading Ease score was 53, which compares to a reading level of >16 years old. The overall mean grade score was 11.9, which compares to a senior high school student in US. CONCLUSION: The mean grade scores of patient resources are considerably higher than the recommended level of 7thgrade or 12–13 years old. Simpler and clearer materials would be more suitable to the general public in the US and internationally. REFERENCES: 1. McKinley J, Cattermole H, Oliver CW. The quality of surgical information on the Internet. J R Coll Surg Edinb. 1999; 44:265–8. 2. Walsh TM1, Volsko TA. Readability assessment of internet-based consumer health information. Respir Care. 2008 Oct; 53:1310–5.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".