Ebola virus disease: an update for anesthesiologists and intensivists
Bibliographic record
Abstract
PURPOSE: Ebola virus disease (EVD) is a viral hemorrhagic fever that is highly transmissible and all too often rapidly fatal. Recent outbreaks in West Africa reveal that this infection has the potential to be transmitted worldwide. Anesthesiologists and intensivists, due to their training in the management of the critically ill, may be called upon to assist in the management of these patients. The focus of this brief review is on the epidemiology, pathogenesis, and management of patients with EVD. SOURCE: Review of the current literature. PRINCIPAL FINDINGS: Ebola virus disease causes severe diarrhea, electrolyte disturbances and other major end-organ dysfunction. Early aggressive resuscitation may reduce the mortality of this disease. There is presently no available vaccine nor cure, with experimental therapies having yielded limited success. Personal protective equipment (PPE) is necessary for all patient contact, and enhanced PPE is required for all aerosol-generating medical procedures. CONCLUSION: Anesthesiologists and intensivists may be called upon to manage patients with EVD. It is important that these clinicians have an appreciation for the epidemiology and pathogenesis of this disease and for the proper utilization of PPE when treating these patients.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".