Caring for Critically Ill Patients with Ebola Virus Disease. Perspectives from West Africa
Bibliographic record
Abstract
The largest ever Ebola virus disease outbreak is ravaging West Africa. The constellation of little public health infrastructure, low levels of health literacy, limited acute care and infection prevention and control resources, densely populated areas, and a highly transmissible and lethal viral infection have led to thousands of confirmed, probable, or suspected cases thus far. Ebola virus disease is characterized by a febrile severe illness with profound gastrointestinal manifestations and is complicated by intravascular volume depletion, shock, profound electrolyte abnormalities, and organ dysfunction. Despite no proven Ebola virus-specific medical therapies, the potential effect of supportive care is great for a condition with high baseline mortality and one usually occurring in resource-constrained settings. With more personnel, basic monitoring, and supportive treatment, many of the sickest patients with Ebola virus disease do not need to die. Ebola virus disease represents an illness ready for a paradigm shift in care delivery and outcomes, and the profession of critical care medicine can and should be instrumental in helping this happen.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".