Assessing the readability, quality and accuracy of online health information for patients with low anterior resection syndrome following surgery for rectal cancer
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.
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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 | 0.005 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".