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
“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:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| 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