Epert Consensus on a Canadian Internal Medicine Ultrasound Curriculum
Bibliographic record
Abstract
Ultrasonography is increasingly used at the bedside. In the absence of an already developed curriculum appropriate for Canadian internal medicine training programs, 13 representatives from internal medicine programs in five Western Canadian provinces met for 2 days to develop and propose a consensus-based internal medicine curriculum for training in the bedside use of ultrasonography in a Canadian health care context.All 13 had had interest or leadership role in those programs. The curriculum’s content was based on three overarching principles agreed upon by the group: (1) content should be selected on the basis of clinical or educational need; (2) content should be feasible (i.e., both cognitive and technical components of the curriculum could be reasonably taught and learned in a competency-based manner while minimizing potential risks to patients); and (3) content should be evidence based. A consensusbased curriculum of 16 proposed topics is to be considered for the core internal medicine residency training program (postgraduate year [PGY] 1 to PGY 3), and 22 topics are to be considered for general internal medicine subspecialty training programs (PGY 4 to PGY 5).
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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".