Pediatric neurosurgeons’ philosophical approaches to making intraoperative decisions when encountering an uncertainty or a complication while operating on children
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to explore approaches to intraoperative decision-making in pediatric neurosurgeons when they encounter unexpected events, uncertainties, or complications while operating on children. METHODS: Twenty-six pediatric neurosurgeons from 12 countries around the world were interviewed using a semistructured interview guide. The grounded theory method of data collection and analysis was used. Analysis involved line-by-line coding and was inductive, with codes and categories emerging from participants' narratives. RESULTS: When asked to discuss the strategies they used to make intraoperative decisions, neurosurgeons reported three distinct approaches that formed a philosophy of practice. This included the theme of professional practice-with the subthemes of preparing for uncertainty, doing no harm, being creative and adaptive, being systematic, and working on teams. The second theme pertained to patient and caregiver practices-with the subthemes of shared decision-making and seeing the whole patient. The third theme involved surgeon practice-with the subthemes of cultivating self-awareness and learning from experience. CONCLUSIONS: Pediatric neurosurgeons have a structured, diverse, and well-thought-out analytical philosophy and practice regarding intraoperative decision-making that encompasses a range of approaches including the following: doing no harm, cultivating self-awareness, and seeing the whole patient; and concrete practices such as preparing in advance for uncertainty, working on teams, and learning from experience. These philosophies and practices can be structured and codified in order to teach residents how to develop intraoperative judgment techniques.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".