Tailoring study design to each stage of surgical innovation: the ideal recommendations
Bibliographic record
Abstract
The pathway of surgical innovation is complex. Inherent ethical and practical characteristics make scientific evaluation of new techniques or devices by a definitive randomized controlled trial (RCT) challenging. The IDEAL Collaboration (http://www.ideal-collaboration.net) Framework for evaluating surgical innovation describes a five stage process - Idea, Development, Exploration, Assessment and Long-term study.(1) Early stage studies should be designed to facilitate and prepare the way for a rigorous evaluation by RCT. IDEAL Recommendations in the early stages (Idea/Development) emphasise prospective designs, transparency and full reporting in open registries, to provide reliable data early in the innovation development process. At the Exploration stage, prospective observational studies need to address factors such as case-mix, learning and outcomes, building co-operatively and explicitly towards a definitive evaluation study, preferably an RCT, optimising the contribution of data from non-randomised prospective evaluations (Assessment stage). The Long-term stages should be characterised by registry-based surveillance for both new procedures and devices. IDEAL proposals for high quality RCTs of surgical procedures focus on three key areas: definition of the intervention; who delivers the intervention and preferences of surgeons and patients. IDEAL Recommendations identify modifications to study design which may help address these difficult areas. We will describe examples of good practice using these suggested methods. Everyone involved in evaluating surgical innovations is invited to join the IDEAL Collaboration community and help further evolve methodology and reporting standards for robust trials in surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.666 | 0.824 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.039 | 0.039 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".