Quantifying Difficulty in Endoscopic Endonasal Surgery—A Modified Delphi Method Approach
Bibliographic record
Abstract
Importance Surgical difficulty quantification is crucial to develop educational curricula for progressive training. This need remains unmet in endoscopic endonasal surgery (EES). Objective The objective was to classify the basic skills and procedures of EES according to the perceived difficulty by experts to build a progressive curriculum. Design Three-round modified Delphi study. Setting E-survey with an international panel of EES experts. Participants Fifty-nine international experts were selected on their publications in the field of EES and medical education and invited to participate. Fifteen experts from 5 countries completed the survey. Intervention A 3-round modified Delphi study was conducted. Main Outcome Measures In round#1, basic skills and EES procedures were identified and the basic skills required for these procedures were listed. In round#2, the expert panel ranked the difficulty of basic skills, and in round#3, the difficulty of the EES procedures. The basic skills were grouped into 3 categories of increasing difficulty, and the subsequently-calculated difficulty of each EES procedure was compared with the experts’ score. Consensus was defined at ≥80% agreement. Results Twenty-three basic skills and 26 EES procedures were identified. Basic skills were ranked in 3 groups of increasing difficulty, from easiest (navigation with a 0° telescope) to most difficult (suturing in the nasal cavity), with ≥80% agreement. The least difficult procedure ranked was polypectomy, whereas the most difficult was Vidian neurectomy. The correlation between the expert score and the calculated score reached an R 2 of .75, reflecting the cumulative effect of these required competencies on procedure difficulty. Conclusions Consensus was achieved to rank basic skills and EES procedure according to the perceived difficulty by experts. Relevance This study provides a basis for quantifying surgical difficulty in EES and pave the way for developing a progressive educational curriculum.
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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 | 0.080 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".