Understanding the effectiveness of informed consent in pediatric surgery
Bibliographic record
Abstract
Background: The consent conversation is an essential part of the pre-operative decision-making process. The manner in which this consent conversation is led has significant consequences, yet the process is imperfect. We aim to improve the teaching of effective consenting processes in pediatric surgery by identifying and sharing evidence based on the literature and a clinical study. Ultimately, our research addresses the knowledge gap surrounding the effectiveness of informed consent and emphasizes the importance of the patient perspective in the process. Methods: The first phase of this project is a systematic literature review identifying the best practices of informed consent, followed by two clinical phases that involve pediatric surgeons at the Montreal Children's Hospital. The second phase consisted of interviews in which the surgeons will be asked to consent a standardized parent for their child's surgery. The videos were filmed and evaluated using a questionnaire by patient’s and families of various medical and surgical backgrounds.Results: Our research has identified strengths and weaknesses of the current informed consent process in pediatric surgery. Aspects of the process that have been found to be effective include the use of multimedia, adequate time, surgeon empathy, the possibility of multiple conversations, and adopting an individualized shared decision-making approach. Some areas of the process that may need improvement include better use of language by the surgeon, more time for questions, recognition of parental anxiety and improvement of recall, and consideration of the child and their rights. Conclusions: Our results highlight potential areas for improvement in the current process. Upon completion of this work, we hope to compile the information from the clinical study and the literature concerning effective consent processes and use it to create new consenting videos that can be disseminated as a teaching resource for medical students and surgical residents
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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 | 0.562 | 0.707 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.017 | 0.039 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".