Evolution of the Research Literature and the Scientific Community of Alzheimer’s Disease from 1983-2017: A 35-Year Survey
Bibliographic record
Abstract
This study surveys the development of Alzheimer's disease (AD) in the research literature, the scientific community, and the journals containing AD papers over a 35-year period. Research papers on AD published from 1983 to 2017 in journals indexed in the Web of Science were analyzed in seven five-year periods. The number of AD papers increased from 1,095 in 1983-1987 to 50,532 by 2013-2017 and in the same time period, the number of participating countries went from 27 to 152. The US was the most prolific country throughout, followed by several European countries, Canada, Australia, and Japan. Asian countries have emerged and by 2013-2017, China surpassed all but the US in productivity. Countries in Latin America and Africa have also contributed to AD research. Additionally, several new non-governmental institutions (e.g., ADNI, ADI) have emerged and now play a key role in the fight against AD. Likewise the AD scientific publishing universe evolved in various aspects: an increase in number of journals containing AD papers (227 journals in 1983-1987 to 3,257 in 2013-2017); appearance of several AD-focused journals, e.g., Alzheimer's & Dementia, Journal of Alzheimer's Disease; and the development of special issues dedicated to AD. Our paper complements the numerous extant papers on theoretical and clinical aspects of AD and provides a description of the research landscape of the countries and journals contributing papers related to AD.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.049 | 0.068 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".