SMART Stone Multidisciplinary Team (MDT) and patient care: recommendations for the adult high-risk kidney stone patient pathway.
Bibliographic record
Abstract
Purpose The SMART Stone Multidisciplinary Team (MDT) recommendations aim to provide guidance on the role of the MDT in the early identification, referral and assessment of adult high-risk recurrent kidney stone formers to advance patient care.Methods Recommendations were developed by the expert Steering Committee (SC) comprising of three Urologists, one Nephrologist, and two Biochemists/Geneticists from the UK, Spain, Germany, and Italy. These recommendations were voted on by invited specialists via an online survey to determine their level of agreement, from 'strongly agree' to 'strongly disagree'. With an agreement threshold set at ≥ 70%, the SC reviewed the survey results, additional comments, and any areas of disagreement before finalizing the recommendations.Results A total of 44 recommendations were developed by the SC designed to support the set-up of an ideal MDT. Thirteen core recommendations were chosen as being highest priority and were voted on by 29 invited specialists from 19 countries across Europe, Canada, East Asia, South/Southeast Asia, and the Middle East. All 13 core recommendations reached the ≥ 70% agreement threshold. The remaining 31 recommendations were voted on by those specialists who opted-in to partake in the extended questionnaire. Fifteen specialists provided their responses from 14 different countries. All 31 recommendations reached the ≥ 70% agreement threshold.Conclusions An ideal MDT process can achieve comprehensive, high-quality, and coordinated patient care, which is especially useful for patients with complex stone diseases. A high level of agreement was reached in areas relating to the implementation of an ideal MDT in identifying high-risk stone formers.
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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 | 0.028 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".