Labels, Language, and Other Strategies to Improve Communication About Lower Grade Forms of Ductal Carcinoma In Situ of the Breast: A National Delphi Survey
Bibliographic record
Abstract
Purpose: This study is aimed at generating consensus among women who had ductal carcinoma in situ (DCIS) and healthcare professionals on how to improve communication about low‐risk forms of DCIS and reduce affected women’s diagnosis‐related confusion and anxiety. Methods: We conducted a two‐round online Delphi survey with affected women and professionals from across Canada. They rated items sourced from prior research and key informant interviews on a 7‐point Likert scale. We retained items rated 6 or 7 by ≥ 80% of panelists. Results: Thirty‐seven panelists (17 women, 20 professionals) completed Round 1 and 94.6% of those completed Round 2. Of 42 items rated, 18 were retained, 13 discarded, and 11 did not achieve consensus to retain or discard. Women and professionals agreed on 3 language approaches (use plain language, distinguish DCIS from invasive breast cancer, specify the risk of recurrence and spread) and 9 other strategies to help discuss DCIS (e.g., use visual aids, provide or refer women to culturally tailored DCIS‐specific information, ensure physicians can access interpreters). Based on rating and comments, women were more enthusiastic than professionals about referring to abnormal cells rather than DCIS and scheduling longer or follow‐up visits to address concerns. To disseminate these findings, panelists recommended public awareness campaigns for women and continuing education and professional society endorsement for physicians. Conclusion: These findings address gaps in prior research that recommended changing the DCIS label, but had not fully explored label preferences, or identified other ways to improve and support communication about DCIS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".