A Practical Methodological Approach Towards Identifying Core Competencies in Medical Education Based on Literature Trends
Bibliographic record
Abstract
BACKGROUND: Competency-based medical education (CBME) is gaining momentum in postgraduate residency and fellowship training. While randomized trials, consensus statements, and practice guidelines can help delineate some of the core competencies for CBME, they are not applicable to all clinical scenarios. OBJECTIVE: To propose and assess the feasibility of a practical methodology for addressing this issue using radiosurgery for vestibular schwannoma (VS) science as an example. METHODS: The Web of Science electronic database was searched using relevant terms. A 3-step review of titles and abstracts was used. Studies were classified independently and in duplicate as either efficacy or effectiveness analyses. Cohen's kappa score was used to assess inter-rater agreement. RESULTS: Overall, 1818 surgical and 943 radiosurgical publications were identified. The number of effectiveness studies surpassed that of efficacy studies in the late 1980s for surgical studies, and in the early-to-mid 1990s among radiosurgical studies. The publication rate was higher for radiosurgery in the mid 1990s, but it paralleled that of surgical studies beyond the early 2000s. Variations in this overall trend corresponded to the emergence of studies that assessed the role of endoscopy and the utility of dose reduction in radiosurgery. CONCLUSION: We have confirmed the feasibility and accuracy of this objective methodological approach. By understanding how the peer-reviewed literature reflects actual practice interests, educators can tailor curricula to ensure that trainees remain current. While further validation studies are needed, this methodology can serve as a supplemental strategy for identifying additional core competencies in CBME.
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 | 0.631 | 0.744 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.057 | 0.031 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".