Aids to Improve Statistical Risk Communication in Patients Consenting for Surgery and Interventional Procedures: a Systematic Review
Bibliographic record
Abstract
ABSTRACT Objective Evaluate the effect of risk communication tools on the understanding of statistical risk of complications occurring in patients undergoing a surgical or interventional procedure. Summary Background Data Informed consent is an essential process in clinical decision-making, through which healthcare providers educate patients about the benefits, risks and alternatives of a procedure. Numerical risk information is by nature probabilistic and difficult to communicate. Aids which support statistical risk communication and studies assessing their effectiveness are needed. Methods A systematic search was performed across Medline, Embase, PsycINFO, Scopus and Web of Science until July 2021 with a repeated search in September 2022. Studies examining risk communication tools (e.g. informative leaflets, audio-video) in adults (age>16) patients undergoing a surgical or interventional procedure were included. Studies only assessing understanding of non-statistical aspects of the procedure were excluded. Both randomised control trials (RCTs) and observational studies were included. Cochrane risk-of-bias and the Newcastle-Ottawa Scale were used to assess the quality of studies. Due to heterogeneity of the studies, a narrative synthesis was performed (PROSPERO ID: CRD42022285789). Results A total of 4348 articles were identified and following abstract and full-text screening a total of 11 articles were included. 8 studies were RCTs and 3 were cross-sectional. The total number of adult patients was 1030. The most common risk communication tool used was additional written information (n=7). Of the 8 RCTs, 5 showed statistically significant improvements in the intervention group in outcomes relating to recall of statistical risk. Quality assessment of RCTs found some concerns with all studies. Conclusions Risk communication tools appear to improve recall of statistical risk. Additional prospective trials are warranted which can compare various aids and determine the most effective method of improving patient understanding.
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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 | 0.027 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".