Evolutionary medicine: update on the relevance to family practice.
Bibliographic record
Abstract
OBJECTIVE: To review the relevance of evolutionary medicine to family practice and family physician training. QUALITY OF EVIDENCE: Articles were located through a MEDLINE search, using the key words evolution, Darwin, and adaptation. Most references presented level III evidence (expert opinion), while a minority provided level II evidence (epidemiologic studies). MAIN MESSAGE: Evolutionary medicine deals with the interplay of biology and the environment in the understanding of human disease. Yet medical schools have virtually ignored the need for family physicians to have more than a cursory knowledge of this topic. A review of the main trends in this field most relevant to family practice revealed that a basic knowledge of evolutionary medicine might help in explaining the causation of diseases to patients. Evolutionary medicine has also proven key to explaining the reasons for the development of antibiotic resistance and has the potential to explain cancer pathogenesis. As an organizing principle, this field also has potential in the teaching of family medicine. CONCLUSION: Evolutionary medicine should be studied further and incorporated into medical training and practice. Its practical utility will be proven through the generation of testable hypotheses and their application in relation to disease causation and possible prevention.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".