Development of an Educational Website for Patients With Cancer and Preexisting Autoimmune Diseases Considering Immune Checkpoint Blockers: Usability and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Patients with cancer and an underlying autoimmune disease who are considering immune checkpoint blockers (ICBs) need to know about the benefits and risks of severe immune-related adverse events and flares of the autoimmune condition. OBJECTIVE: This study aims to develop and alpha test an educational website for patients with cancer. METHODS: Learning topics, images, and website architecture (including flow and requirements) were developed and iteratively reviewed by members of a community scientist program, a patient advisory group, and content experts. Alpha testing was performed, measuring the site's usability using the Suitability Assessment of Materials and its acceptability using the Ottawa Acceptability Measure. RESULTS: The website included a home page; general information about ICBs; comprehensive modules on the benefits and risks of ICBs for patients with cancer and preexisting autoimmune diseases; general wellness information; and features such as a quiz, additional resources, and a glossary. For the alpha testing, 9 users assessed the newly developed website. Patient reviewers (n=5) had rheumatoid arthritis, Crohn disease, Sjogren syndrome, or vasculitis. Health care provider reviewers (n=4) were medical oncologists or rheumatologists. The median Suitability Assessment of Materials rating was 75 (IQR 70-79; range 0-100) for patients versus 66 (IQR 57-72; range 0-100) for providers (scores ≥70 indicate no substantial changes needed). Recommendations for improvement, mostly involving navigation and accessibility, were addressed. All participants expressed that the website was acceptable and balanced in terms of discussion of benefits and harms. Because half (2/4, 50%) of the providers suggested we increase the amount of information, we extended the content on the impact of having an autoimmune disease when considering ICB treatment, the probability of flares, and the management of flares in this context. CONCLUSIONS: The feedback led to minor revisions to enhance readability, navigation, and accessibility, ensuring the website's suitability as a decision-making aid. The newly developed website could become a supporting tool to facilitate patient-physician discussion regarding ICBs.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".