Practical guide to the Idea, Development and Exploration stages of the IDEAL Framework and Recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Evaluation of new surgical procedures is a complex process challenged by evolution of technique, operator learning curves, the possibility of variable procedural quality, and strong treatment preferences among patients and clinicians. Preliminary studies that address these issues are needed to prepare for a successful randomized trial. The IDEAL (Idea, Development, Exploration, Assessment and Long-term follow-up) Framework and Recommendations provide an integrated step-by-step evaluation pathway that can help investigators achieve this. METHODS: A practical guide was developed for investigators evaluating new surgical interventions in the earlier phases before a randomized trial (corresponding to stages 1, 2a and 2b of the IDEAL Framework). The examples and practical tips included were chosen and agreed upon by consensus among authors with experience either in designing and conducting IDEAL format studies, or in helping others to design such studies. They address the most common challenges encountered by authors attempting to follow the IDEAL Recommendations. RESULTS: A decision aid has been created to help identify the IDEAL stage of an innovation from literature reports, with advice on how to design and report the IDEAL study formats discussed, along with the ethical and scientific rationale for specific recommendations. CONCLUSION: The guide helps readers and researchers to understand and implement the IDEAL Framework and Recommendations to improve the quality of evidence supporting surgical innovation.
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.
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 | 0.068 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 it