Generation of Cross‐Specialty Consensus Statements on Soft Tissue Management via a Modified Delphi Method
Bibliographic record
Abstract
BACKGROUND: Soft tissue management (STM) training programs for surgeons are largely tradition based, and substantial differences exist among different surgical specialties. The lack of comprehensive and systematic clinical evidence on how surgical techniques and implants affect soft tissue healing makes it difficult to develop evidence-based curricula. As a curriculum development group (CDG), we set out to find common grounds in the form of a set of consensus statements to serve as the basis for surgical soft tissue education. METHODS: Following a backward planning process and Kern's six-step approach, the group selected 13 topics to build a cross-specialty STM curriculum. A set of statements based on the curriculum topics were generated by the CDG through discussions and a literature review of three topics. A modified Delphi process including one round of pilot voting through a face-to-face CDG meeting and two rounds of web-based survey involving 22 panelists were utilized for the generation of consensus statements. RESULTS: Seventy-one statements were evaluated, and 56 statements reached the 80% consensus for "can be taught as is." CONCLUSIONS: Using a modified Delphi method, a set of cross-specialty consensus statements on soft tissue management were generated. These consensus statements can be used as a foundation for multi-specialty surgical education. Similar methods that combine expert experience and clinical evidence can be used to develop specialty-specific consensus on soft tissue handling.
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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 | 0.215 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".