Training tomorrow’s surgeons: what are we looking for and how can we achieve it?
Bibliographic record
Abstract
The aim of a surgical residency program is to produce competent professionals in a safe and pedagogically efficient environment. For many years, there has been an overemphasis on technical attributes as the fundamental competencies of a trained surgeon. With the advent of new frameworks for defining the outcomes of surgical training, such as CanMeds from the Royal College of Physicians and Surgeons of Canada and the six competencies outlined by the Accreditation Council for Graduate Medical Education in USA, there has been a broadening of the focus of surgical training. Although technical proficiency is definitely an important prerequisite for a successful outcome, other qualities such as intellectual abilities, personality and communication skills, and a commitment to practice are important elements in the profile of a competent surgeon. Recently, there is a growing appreciation for the heterogeneity in achievement of technical competence among our trainees, with some residents able to quickly master technical skill in contrast to others who may never achieve mastery in the technical domain. The questions of how to select, teach and grant privileges for independent practice requires an understanding of the components of surgical competence and implementation of evidence based tools for training and assessment of these competencies.
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.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".