Paradigmatic Approach to Support Personalized Counseling With Digital Health (iKNOW)
Bibliographic record
Abstract
iKNOW is the first evidence-based digital tool to support personalized counseling for women in Germany with a hereditary cancer risk. The counseling tool is designed for carriers of pathogenic gBRCA (germline breast cancer gene) variants that increase the lifetime risk of breast and ovarian cancer. Carriers of pathogenic variants are confronted with complex, individualized risk information, and physicians must be able to convey this information in a comprehensible way to enable preference-sensitive health decisions. In this paper, we elaborate on the clinical, regulatory, and practical premises of personalized counseling in Germany. By operationalizing these premises, we formulate 5 design principles that, we suggest, are specific enough to develop a digital tool (eg, iKNOW), yet wide-ranging enough to inform the development of counseling tools for personalized medicine more generally: (1) digital counseling tools should implement the current standard of care (eg, based on guidelines); (2) digital counseling tools should help to both standardize and personalize the counseling process (eg, by enabling the preference-sensitive selection of counseling contents from a common information base); (3) digital counseling tools should make complex information easy to access both cognitively (eg, by using evidenced-based risk communication formats) and technically (eg, by means of responsive design for various devices); (4) digital counseling tools should respect the counselee's data privacy rights (eg, through strict pseudonymization and opt-in consent); and (5) digital counseling tools should be systematically and iteratively evaluated with the users in mind (eg, using formative prototype testing to ensure a user-centric design and a summative multicenter, randomized controlled trial). On the basis of these paradigmatic design principles, we hope that iKNOW can serve as a blueprint for the development of more digital innovations to support personalized counseling approaches in cancer medicine.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".