Enhancing the Case Log by Coding the Level of Trainee Participation in Vascular Interventional Radiology Procedures
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to describe a new method for coding trainee participation in vascular interventional radiology procedures. MATERIALS AND METHODS: From July 2008 through June 2009, all interventional radiology fellows maintained an enhanced case log at our institution; 748 unique cases were logged by procedure type, supervising physician, and level of participation in the case. Level of participation was classified on a 5-point scale that included designations for observation, first assistant, performance of basic techniques, performance of advanced techniques, and primary operation. Descriptive statistics of participation scores were calculated for each quarter and were analyzed by procedure type and by teaching faculty member. RESULTS: As expected, analysis by procedure type showed that average participation scores increased from one quarter to the next in most cases. By the fourth quarter, the modal participation score was 5, indicating primary operation or performance of multiple critical steps. Analysis by teaching faculty member revealed three patterns: those attending physicians facilitating increasing levels of participation in every quarter, those facilitating maximal growth within the first 6 months, and those with irregular trainee participation profiles. CONCLUSION: Data from a 5-point participation scale add information to the procedure case log that could be used to quantitatively track the technical progress of trainees while providing education quality feedback to both teaching physicians and program directors.
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".