Shared Decision Making in Surgery: A Meta-Analysis and Full Systematic Review
Bibliographic record
Abstract
Shared decision-making (SDM), the process where physician and patient reach an agreed-upon choice by understanding the values, concerns, and preferences inherent within each treatment option available, has been increasingly implemented in clinical practice to better health care outcomes. Despite the proven efficacy of SDM to provide better patient-guided care in medicine, its use in surgery has not been studied widely. A search strategy was developed with a medical librarian. It included nine databases from inception until December 2018. After a 2-person title and abstract screen, full-text publications were analyzed in detail. A meta-analysis was done to quantify the impact of SDM in surgical specialties. In total 5,596 studies were retrieved. After duplicates were removed, titles and abstracts were screened, and p-values were recorded, 140 (45 RCTs and 95 cross-sectional studies) were used for the systematic review and 42 for the meta-analyses. Most of the studies noted decreased intervention rate (8 of 14), decisional conflict (13 of 16), and decisional regret (3 of 3), and an increased decisional satisfaction (9 of 12), knowledge (19 of 20), SDM preference (6 of 8), and physician trust (3 of 4) when using SDM. Time increase per patient encounter was inconclusive. The meta-analysis showed that despite high heterogeneity, the results were significant. Far from obviating surgical immediacy, these results suggest that SDM is vital for the best indicators of care. With decreased conflict and anxiety, increasing knowledge and satisfaction, and creating a more whole, trusting relationship, SDM appears to be beneficial in surgery.
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 | 0.026 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".