Information on the Internet about colorectal cancer: patient attitude and potential toward Web browsing. A prospective observational study
Bibliographic record
Abstract
BACKGROUND: Patients with colorectal cancer who seek to improve their knowledge of health and treatment options can now access in a few seconds data that would previously have required hours of research. Our aim was to evaluate the attitudes of patients toward Web browsing for information on colorectal cancer. METHODS: We surveyed all patients attending a colorectal cancer follow-up clinic between January and August 2007 on their use of the Internet to obtain information on colorectal cancer. RESULTS: In all, 439 patients with mean age of 68.6 years participated in the study. Of these, 24% reported using the Internet to obtain colorectal cancer information. Most participants used the Google search engine. Only 13% of participants confirmed that colorectal cancer information on the Internet was helpful in decision-making. Patients under the age of 65 years were more likely to have Internet access (p < 0.001), more likely to use the Internet to find colorectal cancer information (p = 0.005) and more likely to access a site recommended by a colorectal specialist (p = 0.002). Among Internet users, men were slightly more likely than women to use the Internet, although the difference was not significant (p = 0.20). CONCLUSION: The Internet is a useful tool for disseminating information about colorectal cancer. The best sites are still difficult for patients to distinguish from the thousands of sites returned by search engines. This study demonstrates that the level of potential interest is sufficient to justify the development of a departmental or regional colorectal cancer network of websites and indicates areas of interest 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".