A Consensus Definition of Creativity in Surgery: A Focus Group and Modified Delphi Study
Bibliographic record
Abstract
Despite its importance in solving ill-defined problems in the operating room and fueling innovation, creativity in surgery has been grossly understudied by the research community. To facilitate its valid and reliable scientific study, we aimed to construct a definition of creativity specific to the domain of surgery, informed by existing conceptualizations of creativity and developed with surgeon input. We began by conducting a focus group of 11 highly experienced surgeons, with the aim of collecting data on surgeons’ perceptions, attitudes, and beliefs about creativity in surgery. We then developed a Delphi survey which proposed five surgery-specific definitions of creativity, based on existing definitions of creativity sourced through a systematic literature search and the focus group results. A total of 60 surgeons participated in the Delphi survey (34.8% response rate), with representation from all surgical sub-specialties. After two rounds, we achieved consensus on the following definition: “creativity in surgery is the interaction between expertise, environment, process, and motivation, enabling the generation of novel and useful products, ideas, and solutions to unsolved surgical problems or problems others have solved differently.” We discuss the domain-specific intricacies of the definition, as well as the implications of this study on creativity research.
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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 | 0.165 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".