Bibliographic record
Abstract
“See one, do one” is not the best way to teach the complex technical procedures needed in many hospital based specialties For many patients, a successful clinical outcome depends on having a well performed technical procedure. Crucial for surgeons, technical competence is becoming an important element of training for many hospital based specialists: interventional radiologists, cardiologists, gastroenterologists, endovascular therapists, and others. “See one, do one” is no longer appropriate for educating health professionals to perform complex procedures. Graduated independence, the hallmark of the approach to teaching procedural skills, is being challenged by concerns for patients’ safety, the skyrocketing complexity of procedures, and a diminishing work week for trainees. Finding the balance between patients’ safety and doctors’ training will require a more structured approach to our skills curriculum, including continuous assessment of skills, constructive feedback, and provision of opportunities for deliberate practice in the teaching environment. This paper aims to provide an evidence based algorithm for procedural skills training. It focuses on teaching technical skills, which are just one component of a successful procedure—others are clinical judgment, communication, and team work. Currently, training in technical procedures is often unsystematic and unstructured. Educational tools that have been validated are often underutilised,1 and evidence is growing that adjunctive methods for procedural teaching, such as the use of virtual reality, have not been translated into clinical practice. Teaching communities worldwide would benefit from standardised validated curriculums that use proved technology for teaching technical competence effectively, minimise wasted time, and focus on the breadth of skills needed for a specific practice. ### Pre-patient training Pretraining for technical skills should involve three major components, which should be done outside the clinical setting:
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.024 |
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".