Using Tablet-Based Technology in Patient Education about Systemic Therapy Options for Early-Stage Breast Cancer: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Patient education in early-stage breast cancer has been shown to improve patient well-being and quality of life, but it poses a challenge given the increasingly complex regimens and time constraints in clinical practice. Technology-aided teaching in the clinic could help to improve the understanding of adjuvant systemic therapy for patients. In this prospective pilot study, we used a clinician-administered, tablet-based teaching aid to teach patients with early-stage breast cancer about adjuvant systemic therapy. METHODS: Participation was offered to newly diagnosed patients with early-stage breast cancer presenting for their first medical oncology visit at a provincial cancer centre. Participants were shown a tablet-based presentation describing procedures, rationales, risks, and benefits of adjuvant systemic therapy as an adjunct to a discussion with the medical oncologist. After the clinic visit, participants completed a questionnaire measuring satisfaction with the visit and knowledge of the treatment plan discussed. RESULTS: The 25 patients recruited for the study had a mean age of 57 years. An offer of upfront chemotherapy alone was made to 12 participants (48%), chemotherapy with trastuzumab to 4 (16%), and hormonal therapy to 9 (36%). Correct answers to all questions related to treatment knowledge were given by 22 patients (88%). Satisfaction with the clinic visit was high (mean satisfaction score: 4.53 ± 0.1 of a possible 5). CONCLUSIONS: We found that a tablet-based presentation about adjuvant systemic therapy was satisfactory to patients with early-stage breast cancer and that knowledge retention after the clinic visit was high. Tablet-based teaching could be a feasible and effective way of educating patients in the breast oncology clinic and warrants further investigation in randomized studies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".