Predictive Enrichment in Kidney RCTs: Is Albuminuria the Answer?
Bibliographic record
Abstract
Introduction To evaluate the impact of non-technical skills (NTS) on team performance, workload and clinical outcomes. Methods The operating room (OR) environment of 20 robot-assisted radical prostatectomies performed by three different surgeons was recorded. Trained observers assessed NTS utilising the Non-Technical Skills for Surgeons (NOTSS) questionnaire. Associations between NOTSS scores, teamwork attributes (anticipation and inconveniences), workload (measured by National Aeronautics and Space Administration-Task Load Index (NASA-TLX)) and clinical outcomes (operative time, blood loss and surgical complexity) were determined using logistic regression and Pearson correlation. Results 1780 requests were observed, 703 (39%) were non-verbal. Utilisation of non-verbal requests differed significantly among surgeons (26%, 36% and 44%, p<0.001). Anticipation was significantly associated with ‘Situational Awareness’ (OR 2.59, 95% CI 1.52 to 4.38, p<0.001), ‘Decision Making’ (OR 0.42, 95% CI 0.33 to 0.55, p<0.001) and ‘Communication and Teamwork’ (OR 0.43, 95% CI 0.25 to 0.74, p=0.002) domains. Inconveniences were significantly associated with ‘Situational Awareness’ (OR 0.21, 95% CI 0.08 to 0.59, p=0.003), ‘Decision Making’ (OR 2.73, 95% CI 1.53 to 4.86, p<0.001), and ‘Leadership’ (OR 0.62, 95% CI 0.41 to 0.94, p=0.03). There was a significant positive correlation between NOTSS scores and perceived physical and mental workload measures of NASA-TLX, as well as self-perceived performance. There was no significant association between NOTSS scores and any of the investigated clinical outcomes. Conclusion NTS in the OR were associated with team efficiency, fewer surgical flow disruptions and an improved self-perceived performance.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".