Cancer patients and the Internet: A randomized controlled trial (RCT) evaluating an intervention to facilitate physician and patient information exchange from the Internet (I)
Bibliographic record
Abstract
6139 Background: Prior research showed that only a small proportion of patients with cancer use the Internet to find information and discuss it with their oncologist. Purpose: To evaluate the effectiveness of using a question prompt sheet (QPS) to promote discussion of health-related I information between patients and oncologists. Methods: Patients were randomized to receive or not the QPS and then assessed pre-and post- consultation for discussion of I information with their physician, satisfaction with the medical encounter, anxiety (Speilberger STAI), information and decision making preferences, plus I use. Results: 103 patients with breast, lung, GU or GI cancer visiting an oncologist participated (response rate 78%). Mean age was 66 yrs. (SD 10.08), 39% were female, 85% were married and 33% had some post-secondary education. There were no differences in baseline patient characteristics between groups with the exception that patients in the QPS group preferred to be less involved in decision making vs. control group. 14.6% of those randomized to QPS discussed I information in the clinical encounter versus 10.9% of controls (p=0.476). There was no significant difference between groups with respect to change in anxiety scores (pre- vs. post-consultation) or patient satisfaction with the consultation. 46 patients had used the I to seek any type of information, 39 sought health information and 38 sought cancer information. Patients reported using the I to obtain more detailed information about their cancer/treatment options (71%), to clarify information given to them by a physician (58%), to gain a better understanding of medical terms (23%), and to find/access decision aids (47%). Two thirds of patients sought information before they had an initial consultation with an oncologist. Conclusions: A small proportion of patients discussed information from the Internet with their oncologist (13%). The QPS was not effective in promoting this behavior. Other strategies need to be evaluated to encourage patients to bring information from other sources to the clinical consultation. No significant financial relationships to disclose.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".