Reporting ChAracteristics of cadaver training and sUrgical studies: The CACTUS guidelines
Bibliographic record
Abstract
INTRODUCTION: Recent systematic reviews highlighted increasing use of cadaveric models in the surgical training, but reports on the characteristics of the models and their impact on training are lacking, as well as standardized recommendations on how to ensure the quality of surgical studies. The aim of our survey was to provide an easy guideline that would improve the quality of the studies involving cadavers for surgical training and research. METHODS: After accurate literature review regarding surgical training on cadaveric models, a draft of the CACTUS guidelines involving 10 different items was drawn. Afterwards, the items were improved by questionnaire uploaded and spread to the experts in the field via Google form. The guideline was then reviewed following participants feedback, ergo, items that scored between 7 and 9 on nine-score Likert scale by 70% of respondents, and between 1 and 3 by fewer than 15% of respondents, were included in the proposed guideline, while items that scored between 1 and 3 by 70% of respondents, and between 7 and 9 by 15% or more of respondents were not. The process proceeded with Delphi rounds until the agreement for all items was unanimous. RESULTS: In total, 42 participants agreed to participate and 30 (71.4%) of them completed the Delphi survey. Unanimous agreement was almost always immediate concerning approval and ethical use of cadaver and providing brief outcome statement in terms of satisfaction in the use of the cadaver model through a short questionnaire. Other items were subjected to the minor adjustments. CONCLUSION: 'CACTUS' is a consensus-based guideline in the area of surgical training, simulation and anatomical studies and we believe that it will provide a useful guide to those writing manuscripts involving human cadavers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".