The 2014 Ebola Virus Outbreak in West Africa: Current Perspectives for Prevention and Treatment
Bibliographic record
Abstract
transmission remained persistent and widespread in Guinea, Liberia, and Sierra Leone, especially in the capital cities of these countries.Notably, on a smaller scale, there has also been in 2014 a separate and independent outbreak of Ebola virus in the Democratic Republic of the Congo, which involved 66 cases and resulted in 49 deaths so far, although the situation there seems to remain under relative control. 5ola virus, a member of the Filoviridae family, was discovered in 1976 as the causative agent of severe hemorrhagic fevers in Sudan and Zaire.[6][7][8][9][10] It was only the second Filovirus identified in humans, after the Marburg virus a few years before.11,12 Besides high fever and rapid health deterioration, the Ebola virus disease is characterized mainly by drastic alterations of the immune system due to viral replication in dendritic cells and macrophages, leading to suppression of interferon production along with massive release of inflammatory proteins, and by a weakening of blood vessel walls, resulting in blood leakage, hypotension, and serious haemorrhages.The virus affects directly and indirectly several organs and systems in the body, notably the liver, the adrenal gland and the gastro-intestinal tract.13
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".