Bibliographic record
Abstract
Medical education research is not as well understood or established as is basic science or clinical research. The reasons for this are many, but most importantly, there is insufficient funding for medical education research and a dearth of skilled and experienced medical education researchers. There is no nationally centralized force to build and sustain a medical education research enterprise. Yet faculty and training programs are held accountable for the quality of patient care rendered by those that they train. New regulatory requirements at all levels of physician training demand assurance that physicians are competent to practice in the current health care environment and provide optimal patient care. Documenting the relationship between education and patient outcomes represents one of the biggest challenges and greatest opportunities in medical education research. There is no research infrastructure in place to support such outcomes studies. The majority of medical education research that is currently being done is supported by volunteer faculty time and resources. This is not a viable model to sustain a medical education research mission. Compared with medicine in general, these challenges are multiplied in radiology, where there are relatively fewer extramural research dollars available and skilled investigators to carry out radiology education research. Building a critical mass of radiology education researchers through education fellowship programs specific to radiology and mobilizing the existing radiology education researchers into one group with a shared vision are opportunities for elevating the status of radiology education research.
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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".