Shared decision making in surgery: A scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Shared decision making (SDM) has been increasingly implemented to improve health-care outcomes. Despite the mixed efficacy of SDM to provide better patient-guided care, its use in surgery has not been studied. The aim of this study was to systematically review SDM application in surgery. DESIGN: The search strategy, developed with a medical librarian, included nine databases from inception until June 2019. After a 2-person title and abstract screen, full-text publications were analysed. Data collected included author, year, surgical discipline, location, study duration, type of decision aid, survey methodology and variable outcomes. Quantitative and qualitative cross-sectional studies, as well as RCTs, were included. RESULTS: A total of 6060 studies were retrieved. A total of 148 were included in the final review. The majority of the studies were in plastic surgery, followed by general surgery and orthopaedics. The use of SDM decreased surgical intervention rate (12 of 22), decisional conflict (25 of 29), and decisional regret (5 of 5), and increased decisional satisfaction (17 of 21), knowledge (33 of 35), SDM preference (13 of 16), and physician trust (4 of 6). Time increase per patient encounter was inconclusive. Cross-sectional studies showed that patients prefer shared treatment and surgical treatment varied less. The results of SDM per type of decision aid vary in terms of their outcome. CONCLUSION: SDM in surgery decreases decisional conflict, anxiety and surgical intervention rates, while increasing knowledge retained decisional satisfaction, quality and physician trust. Surgical patients also appear to prefer SDM paradigms. SDM appears beneficial in surgery and therefore worth promoting and expanding in use.
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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 | 0.024 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".