The Importance of Basic Science and Research Training for the Next Generation of Physicians and Physician Scientists
Bibliographic record
Abstract
Now more than ever, medical educators at all levels are tasked with delivering foundational concepts and content necessary for the next generation of physicians and physician scientists to incorporate evidence-based medicine into their clinical practice and research. Physicians need the skills to evaluate the quality and relevance of content that they are incorporating to their expanding medical knowledge database. The emphasis of modern medical education should therefore focus on acquiring, interpreting, and applying new knowledge rather than committing old (and rapidly outdated) knowledge to memory. Whether encountering a patient with an unfamiliar set of symptoms and numerous comorbidities or a gap in mechanistic understanding of a complex biological or physiological process, medical and scientific knowledge must be applied appropriately to be effective. Furthermore, assessments of the outcomes of that application to the patient or an experimental observation must be robust if conclusions are to be reached that advance our understanding of patient care or fundamental biological processes. However, instruction or training in evidence-based medicine presents a challenge to medical educators, because the depth of mechanistic understanding of physiological and pathophysiological process has exploded. Quantum leaps have been made in our understanding of human biology advanced by the solving of DNA structure in 1953 and sequencing of the first human genome in 2003. The depth in our understanding of fundamental processes in human biology and disease is seen at every level, genetic, molecular, cellular, and system wide. However, despite this explosion of scientific knowledge, the time devoted to basic science instruction in most medical school curricula has been shrinking rather than expanding. We should all expect more from our medical students than a snap-chat, wikiknowledge level of basic science processes. Moreover, we must recognize that the process of knowledge acquisition is equally as important, if not more so, than the actual knowledge acquired. Because the recall of stored content can be so rapid and effortless in the digital age, how can we expect the current generation of students to be excited and inspired by delivery of content merely for content’s sake? In 1910 (1), Abraham Flexner published his report on the state of medical education in the United States and Canada that fundamentally reformed medical schools in North America with curricular and policy recommendations that remain essentially intact in today’s landscape of medical education. Flexner himself was instrumental in the founding of one of the first American medical schools (ie, the
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 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".