Publications in PubMed on Ebola and the 2014 outbreak
Bibliographic record
Abstract
In this research note we examine the biomedical publication output about Ebola in 2014. We show that the volume of publications has dramatically increased in the past year. In 2014 there have been over 888 publications with 'ebola' or 'ebolavirus' in the title, approximately 13 times the volume of publication of 2013. The rise reflects an impressive growth starting in the month of August, concomitant with or following the surge in infections, deaths and coverage in news and social media. Though non-research articles have been the major contributors to this growth, there has been a substantial increase in original research articles too, including many papers of basic science. The United States has been the country with the highest number of research articles, followed by Canada and the United Kingdom. We present a comprehensive set of charts and facts that, by describing the volumes and nature of publications in 2014, show how the scientific community has responded to the Ebola outbreak and how it might respond to future similar global threats and media events. This information will assist scholars and policymakers in their efforts to improve scientific research policies with the goal of maximizing both public health and knowledge advancement.
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 | 0.007 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.052 | 0.090 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".