Labels, Language, and Other Strategies to Improve Communication About Lower Grade Ductal Carcinoma in Situ: Theoretical Review
Bibliographic record
Abstract
Ductal carcinoma in situ (DCIS) is when abnormal cells are found in the milk ducts of the breast, but they have not spread outside the ducts. It is not an invasive cancer, but it can sometimes turn into cancer over time if not treated. Women with low or intermediate grade DCIS are counseled to undergo standards of care, which may include surgery, radiation, and/or endocrine therapy even though DCIS may not develop into breast cancer, prompting confusion and long-lasting anxiety. The purpose of this study was to identify ideal labels, language, and other strategies to improve communication about DCIS. We conducted a theoretical review of 12 studies published between 2011 and 2022 and analyzed our findings with communication accommodation theory (CAT). Women and clinicians differed in initial orientation and psychological accommodation. Women were confused and anxious because clinicians employed labels such as pre-cancer or stage 0 cancer, but referred to it as "only" DCIS. Women preferred that clinicians refer to "abnormal cells" and distinguish DCIS from invasive breast cancer. In contrast, clinicians incorrectly believed that women understood that pre-cancer or stage 0 cancer distinguished DCIS from invasive breast cancer, and rather than explaining, referred women to other sources of information. However, women and clinicians agreed on several ways to improve communication: approximation (e.g. plain language), interpretability (e.g. visual aids), interpersonal control (e.g. take time to answer questions), discourse management (e.g. discuss risk of spread/recurrence) and emotional expression (e.g. address concerns).
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".