National consensus recommendations on patient-centered care for ductal carcinoma in situ
Bibliographic record
Abstract
PURPOSE: The purpose of this research was to generate recommendations on strategies to achieve patient-centered care (PCC) for ductal carcinoma in situ (DCIS). METHODS: Thirty clinicians (surgeons, medical/radiation oncologists, radiologists, nurses, navigators) who manage DCIS and 32 DCIS survivors aged 18 or older were nominated. Forty-six recommendations to support PCC for DCIS were derived from primary research, and rated in a two-round Delphi process from March to June 2018. RESULTS: A total of 29 clinicians and 27 women completed Round One, and 28 clinicians and 22 women completed Round Two. The 29 recommendations retained by both women and clinicians reflected the PCC domains of fostering patient-physician relationship (5), exchanging information (5), responding to emotions (1), managing uncertainty (4), making decisions (9), and enabling patient self-management (5). An additional 13 recommendations were retained by women only: fostering patient-physician relationship (1), exchanging information (3), responding to emotions (2), making decisions (3), and enabling patient self-management (4). Some recommendations refer to processes (i.e., ask questions about lifestyle or views about risks/outcomes to understand patient preferences); others to tools (i.e., communication aid). Panelists recommended a separate consensus process to refine the language that clinicians use when describing DCIS. CONCLUSIONS: This is the first study to generate guidance on how to achieve PCC for DCIS. Organizations that deliver or oversee health care can use these recommendations on PCC for DCIS to plan, evaluate, or improve services. Ongoing research is needed to develop communication tools, and establish labels and language for DCIS that optimize communication.
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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 | 0.148 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".