A global, incremental development method for a web-based prostate cancer treatment decision aid and usability testing in a Dutch clinical setting
Bibliographic record
Abstract
Many new decision aids are developed while aspects of existing decision aids could also be useful, leading to a sub-optimal use of resources. To support treatment decision-making in prostate cancer patients, a pre-existing evidence-based Canadian decision aid was adjusted to Dutch clinical setting. After analyses of the original decision aid and routines in Dutch prostate cancer care, adjustments to the decision aid structure and content were made. Subsequent usability testing (N = 11) resulted in 212 comments. Care providers mainly provided feedback on medical content, and patients commented most on usability and summary layout. All participants reported that the decision aid was comprehensible and well-structured and would recommend decision aid use. After usability testing, final adjustments to the decision aid were made. The presented methods could be useful for cultural adaptation of pre-existing tools into other languages and settings, ensuring optimal usage of previous scientific and practical efforts and allowing for a global, incremental decision aid development process.
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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 | 0.041 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".