Interventions are needed to support patient–provider decision-making for DCIS: a scoping review
Bibliographic record
Abstract
PURPOSE: Prognostic and treatment uncertainty make ductal carcinoma in situ (DCIS) complex to manage. The purpose of this study was to describe research that evaluated DCIS communication experiences, needs and interventions among DCIS patients or physicians. METHODS: MEDLINE, EMBASE, CINAHL and The Cochrane Library were searched from inception to February 2017. English language studies that evaluated patient or physician DCIS needs, experiences or behavioural interventions were eligible. Screening and data extraction were done in duplicate. Summary statistics were used to describe study characteristics and findings. RESULTS: A total of 51 studies published from 1997 to 2016 were eligible for review, with a peak of 8 articles in year 2010. Women with DCIS lacked knowledge about the condition and its prognosis, although care partners were more informed, desired more information and experienced decisional conflict. Many chose mastectomy or prophylactic mastectomy, often based on physician's recommendation. Following treatment, women had anxiety and depression, often at levels similar to those with invasive breast cancer. Disparities were identified by education level, socioeconomic status, ethnicity and literacy. Physicians said that they had difficulty explaining DCIS and many referred to DCIS as cancer. Despite the challenges reported by patients and physicians, only two studies developed interventions designed to improve patient-physician discussion and decision-making. CONCLUSIONS: As most women with DCIS undergo extensive treatment, and many experience treatment-related complications, the paucity of research on PE to improve and support informed decision-making for DCIS is profound. Research is needed to improve patient and provider discussions and decision-making for DCIS management.
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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 | 0.017 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".