Use of internet resources by patients awaiting gastroenterology consultation
Bibliographic record
Abstract
BACKGROUND/AIMS: The purpose of this study is to understand how outpatients awaiting initial gastroenterology consultation seek medical information on the Internet and how wait times affect Internet usage. MATERIALS AND METHODS: A cross-sectional survey of 87 gastroenterology outpatients awaiting consultation was performed at a tertiary care center. RESULTS: Fifty-two patients (60%) utilized the Internet for medical information. The mean age of patients using the Internet was 41 years, whereas the mean age of those not using the Internet was 60 years (p<0.0001). The Internet was used by 71% of females and 47% of males (p<0.05). Regarding the educational level, the Internet was sought by 33% of the patients possessing less than secondary school education, 59% possessing secondary school education, 66% with an undergraduate degree, and 100% with a postgraduate degree (p=0.14). The mean wait time for consultation for patients who utilized the Internet was 158 days, and for patients who did not was 147 days (p=0.60). The most common websites searched were medical, 71%. The most common medical information sought was symptoms and diagnosis by 85% of patients. The reasons for Internet use were wait times for 36% of patients and recommendation by a physician for 10%. Eighty seven percent of the patients who utilized the Internet believed that they suffered from an unidentified disease, whereas 46% of patients who did not utilize the Internet believed the same (p=0.0001). CONCLUSION: Younger patients and females were more likely to use the Internet, but wait times did not affect Internet usage. The Internet is a powerful patient resource; however, further physician guidance is required to help patients identify reliable resources.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".