Shared Decision-Making in Breast Reconstruction for Breast Cancer Patients: A Scoping Review
Bibliographic record
Abstract
For most breast cancer (BC) patients who have undergone a mastectomy, the decision whether to proceed with breast reconstruction (BR) is complicated and requires deliberation. Shared decision-making (SDM) helps to address those needs and promote informed value-based decisions. However, little is known about the SDM status for BR in BC patients. This scoping review describes: 1) basic characteristics of studies on BR SDM in BC patients; 2) factors influencing BR SDM in BC patients; 3) experience and perception of BR SDM in BC patients; and 4) outcome measures reported. This review was performed in accordance with the Arksey and O'Malley methodology. A total of 5 English and 4 Chinese databases were searched, as well as different sources from grey literature. The data extraction form was developed by referring to the objectives and the Ottawa Decision Support Framework (ODSF). Data was analyzed using thematic analysis, framework analysis and descriptive statistics, with findings presented in the tables and diagrams. A total of 1481 records were retrieved and 42 of these included after screening. In 21 (21/42, 50%) of the studies, patient decision aids (PDAs) were utilized, and in 17 (17/42, 40.48%) of the studies, the factors influencing the implementation of SDM were explored. Of these 17 studies, the factors influencing the implementation of SDM were categorized into the following: the patient level (17/17, 100%), the healthcare level (2/17, 11.76%) and the organizational and system level (7/17, 41.18%). A total of 8 (19.05%) of the 42 studies focused on patients' experiences and perceptions of SDM, and all studies used qualitative research methods. Of these 8 studies, a total of 7 (7/8, 87.50%) focused on patients' experiences of SDM participation, and 4 (4/8, 50.00%) focused on patients' perceptions of SDM. A total of 24 studies (24/42, 57.14%) involved quantitative outcome measures, where 49 items were divided into three classifications according to the outcomes of ODSF: the quality of the decision (17/24, 70.83%), the quality of the decision-making process (20/24, 83.33%), and impact (13/24, 54.17%). Although researchers have paid less attention to other research points in the field of SDM, compared to the design and application of SDM interventional tools, the research team still presents some equally noteworthy points through scoping review. For instance, the various factors influencing BC patients' participation in SDM for BR (especially at the healthcare provider level and at the organizational system level), patients' experiences and perceptions. Systematic reviews (SRs) should be conducted to quantify the impact of these different factors on BR SDM. Implementation of scientific theories and methods can inform the exploration and integration of these factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".