Resident Learning and Surgical Risk: How Trainee Participation Affects Patient Safety
Bibliographic record
Abstract
Background: Surgical residents need to operate to become competent surgeons. With a desire to reduce patient complications and standards being set appropriately high for quality of care, surgical supervision has increased over time leading to a proportional decrease in autonomy. Concordantly, graduating residents are increasingly perceived as being unready for independent practice: by themselves, their attendings, and often by patients. Objectives: The objective of this thesis is to assess how risk is currently evaluated, and how resident involvement in a surgical case affects that risk. Methods: The thesis consists of three studies. The first, a systematic review, summarizes perioperative risk prediction models, It evaluates the prediction accuracy of risk prediction models using intraoperative and educational variables differ from those using preoperative variables alone. The second study is a retrospective cohort study correlating resident training level with perioperative morbidity and mortality. The final study explores the relationship of resident autonomy to intraoperative events in recorded operations. Results: We found that few modifiable intraoperative factors are used to predict surgical morbidity and mortality. Team composition and resident involvement were not used in any multivariate risk prediction strategies. After adjusting for patient- and procedural covariates, patient outcomes were not adversely affected by the training level of the assist. Finally, we demonstrated that operative skill and step complexity appear to be related to intraoperative adverse events. Resident autonomy, however, was not correlated with intraoperative events. This tentatively suggests that the degree of autonomy residents are given appears to be safe. Conclusions: Through this work, we have demonstrated that resident involvement and educational metrics are under utilized in estimating patient risk and optimizing surgical teams. We have begun to fill this gap in the literature by demonstrating that resident involvement in surgical procedures is not inherently related to poorer safety outcomes. We hope that this work serves as a basis for patient reassurance: that residents have been getting appropriate supervision in surgery and that education does not compromise surgical quality. Going forward, we aim to continue this work in evidence-based surgical education to inform how best to safely develop the next generation of trainees from novice to surgical mastery.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".