Developing a Research Agenda for The American Society of Colon and Rectal Surgeons
Bibliographic record
Abstract
PURPOSE: By use of a systematic approach, the aim of this project was to survey a group of colorectal specialists and reach a consensus on the research questions of highest importance in terms of clinical care. METHODS: A modified Delphi process was performed. In Round 1 research questions were solicited from members of The American Society of Colon and Rectal Surgeons. A review group categorized the results, combined similar questions, and presented them to The American Society of Colon and Rectal Surgeons membership in Round 2 for prioritizing according to importance. In Round 3 the 50 questions with the highest scores in Round 2 were reranked by The American Society of Colon and Rectal Surgeons membership to produce the 20 highest-priority research questions. RESULTS: A total of 203 respondents in Round 1 submitted 746 questions. The review team reduced these to 105 individual questions encompassing 21 topics in colorectal surgical practice. In Rounds 2 and 3, 399 and 360 respondents, respectively, prioritized the questions presented. The final 20 items included 14 questions related to colorectal cancer, and 6 were on benign disease topics. CONCLUSIONS: The research agenda produced by this study reflects the clinical issues of greatest importance to colorectal surgeons. The results are of potential benefit to researchers, funding organizations, medical journals, and ultimately, patients.
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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 | 0.348 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".