Characterization of Operator Learning Curve for Transradial Coronary Interventions
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: Transradial percutaneous coronary intervention (TR-PCI) improves clinical outcomes compared to the transfemoral (TF) approach. However, inadequate training and experience has limited widespread adoption by interventional cardiologists. METHODS AND RESULTS: Clinical and procedural characteristics for TR-PCI were prospectively collected from 1999 to 2008. To identify minimum case volume for optimum clinical benefit, single-vessel TR-PCI cases were chronologically ranked and stratified into 1 to 50, 51 to 100, 101 to 150 and 151 to 300 case volume groups for operators starting the TR approach at the study institution. Cases by operators with a >300 TR-PCI case volume comprised the control group. TR-PCI failure rates, contrast use, guide usage, and fluoroscopy time were compared among groups. A total of 1672 patients underwent TR-PCI by 28 operators. TR-PCI failure occurred in 4% and was higher in the 1 to 50 case volume group compared to the 51 to 100 (P=0.007) and control (P=0.01) groups. Contrast use was greater in the 1 to 50 group (180±79 mL) compared to the 151 to 300 (157±75 mL, P=0.02) and control (168±79 mL, P=0.05) groups. Fluoroscopy time was higher in the 1 to 50 group (15±10 minutes) compared to the 101 to 150 (13±10 minutes, P=0.04) and control (12±9 minutes, P=0.02) groups. Reasons for TR-PCI failure included spasm (38%), subclavian tortuousity (16%), poor guide support (16%), failed access (10%), and radial loop (7%). Case volume was significantly correlated with TR-PCI failure (β=-0.0076, P=0.0028), and odds of failure was reduced by 32% for each 50 increments in case volume. CONCLUSIONS: TR-PCI success depends on operator experience, and a case volume of ≥50 cases is required to achieve outcomes comparable to experienced operators. These findings have implications both for PCI operators looking to expand their skills and for defining standards for training.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.005 |
| 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) | 0.000 | 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