Evaluation of an Online Education Resource on Radiation Therapy Created for Patients with Postprostatectomy Prostate Cancer and Their Caregivers
Bibliographic record
Abstract
INTRODUCTION: Radiation therapy (RT) after prostatectomy is an important curative treatment option for patients with prostate cancer. It can be delivered immediately after surgery as adjuvant treatment, or after biochemical PSA failure as salvage treatment. There is currently a lack of consensus regarding whether salvage RT in the event of biochemical failure or immediate adjuvant RT is the optimal postprostatectomy RT treatment. Although both types of postprostatectomy RT are generally well tolerated, patients may develop some toxicity that can impact their quality of life and the duration and frequency of treatments can be challenging for patients. It is imperative that patients be provided with evidence-based information so that they are able to make a treatment decision most aligned with their values. METHODS: To help address patients' informational needs, an online education resource was created for patients with prostate cancer considering postoperative RT. Patients and their families were asked to evaluate the effectiveness of this resource using a validated purpose-based information assessment. RESULTS: Nineteen patients were approached and 14 participated, but only five patients returned their evaluations (35%). Sixty percent found the information to be important with regards to each of the six commonly identified purposes in the purpose-based information assessment: organizing, understanding, decision-making, planning, emotional support, and discussing. Only one participant found the information hard to understand and had difficulty finding specific information. DISCUSSION: Patients should be encouraged to actively participate in their treatment decision-making process involving postprostatectomy RT. For patients to make well-informed decisions, patients must be provided with clear and accessible information so that they may understand their disease and the treatment options. CONCLUSION: An online education resource has been developed that most study respondents found clear and helpful for a variety of identified purposes. Overall, this online education resource has the potential to reach a large number of patients and their caregivers who desire specific information and involvement in future treatment decisions.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".