Assessing the readability, quality and accuracy of online health information for patients with low anterior resection syndrome following surgery for rectal cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: Management of low anterior resection syndrome (LARS) requires a high degree of patient engagement. This process may be facilitated by online health-related information and education. The aim of this study was to systematically review current online health information on LARS. METHOD: An online search of Google, Yahoo and Bing was performed using the search terms 'low anterior/anterior resection syndrome' and 'bowel function/movements after rectal cancer surgery'. Websites were assessed for readability (eight standardized tests), suitability (using the Suitability Assessment of Materials instrument), quality (the DISCERN instrument), accuracy and content (using a LARS-specific content checklist). Websites were categorized as academic, governmental, nonprofit or private. RESULTS: Of 117 unique websites, 25 met the inclusion criteria. The median readability level was 10.4 (9.2-11.7) and 11 (44.0%) websites were highly suitable. Using the DISCERN instrument, seven (28.0%) websites had clear aims, two (8.0%) divulged the sources used and four (16.0%) had high overall quality. Only eight (32.0%) websites defined LARS and ten (40.0%) listed all five major symptoms associated with the LARS score. There was variation in the number of websites that discussed dietary modifications (80.0%), self-help strategies (72.0%), medication (68.0%), pelvic floor rehabilitation (60.0%) and neuromodulation (8.0%). The median accuracy of websites was 93.8% (88.2-96.7%). Governmental websites scored highest for overall suitability (P = 0.0079) and quality (P < 0.001). CONCLUSIONS: Current online information on LARS is suboptimal. Websites are highly variable, important content is often lacking and material is too complex for patients.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it