Is basic science disappearing from medicine? The decline of biomedical research in the medical literature
Bibliographic record
Abstract
Explosive growth in our understanding of genomics and molecular biology have fueled calls for the pursuit of personalized medicine, the notion of harnessing biologic variability to provide patient‐specific care. This vision will necessitate a deep understanding of the underlying pathophysiology in each patient. Medical journals play a pivotal role in the education of trainees and clinicians, yet we suspected that the amount of basic science in the top medical journals has been in decline. We conducted an automated search strategy in PubMed to identify basic science articles and calculated the proportion of articles dealing with basic science in the highest impact journals for 8 different medical specialties from 1994 to 2013. We observed a steep decline (40‐60%) in such articles over time in almost all of the journals examined. This rapid decline in basic science from medical journals is likely to affect practitioners' understanding of and interest in the basic mechanisms of disease and therapy. In this Life Sciences Forum, we discuss why this decline may be occurring and what it means for the future of science and medicine.—Steinberg, B. E., Goldenberg, N. M., Fairn, G. D., Kuebler, W. M., Slutsky, A. S., Lee, W. L. Is basic science disappearing from medicine? The decline of biomedical research in the medical literature. FASEB J. 30, 515‐518 (2016). www.fasebj.org
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 | 0.061 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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; 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".