Characterization of Operator Learning Curve for Transradial Coronary Interventions
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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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".