Factors That Influence Intraoperative Decision-Making among Pediatric Neurosurgeons: A Grounded Theory Study
Bibliographic record
Abstract
INTRODUCTION: Pediatric neurosurgery is a subspecialty of medicine that is responsible for diagnosing, managing, and treating neurological disease in children with the use of surgery. Good intraoperative decision-making is critical to ensuring patient safety, yet almost nothing is known about what factors play a role in intraoperative decisions. As such, the purpose of this paper was to explore the factors that influence intraoperative decisions when pediatric neurosurgeons encounter something unexpected or uncertain during surgery. METHODS: The study utilized the grounded theory method of data collection and analysis. Twenty-six pediatric neurosurgeons from 12 countries around the world were interviewed between June and October 2020 about the factors that go into making intraoperative decisions. Data were analyzed line by line and constant comparison was used to examine relationships within and across codes and categories. RESULTS: Pediatric neurosurgeons reflected on 6 factors while operating in order to come to a decision about how to proceed when they encountered an uncertainty or complication. The study findings resulted in a conceptual model that describes how concrete data including biological and technological factors and contextual data including emotional/relational factors, surgeon factors, and cultural factors influence risk assessment when making an intraoperative decision during surgery. CONCLUSIONS: The findings from this research can be used for training and educating surgeons about intraoperative decision-making processes. Pedagogical modules can be developed that include training sessions on factors that may implicitly and explicitly influence thinking processes during an operation. Surgeons may also benefit from having open discussions with surgical colleagues about the rich, emotional, intellectual scope of the work that they do with all the challenges that these relationships can bring into decision-making in the operating room.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".