Ebola virus disease in pregnancy and anesthetic considerations
Bibliographic record
Abstract
Ebola virus disease (EVD) is often lethal, mortality rates range from 50% to over 90%, depending on the patient population, viral strain, and access to medical care. During pregnancy, the morbidity and mortality from the viral disease has been suggested to be among the highest of any affected patient population. According to the existing literature, which is confined to a few small case series in Africa, the risk of spontaneous fetal loss is high and there have been no known neonatal survivors. The mode of EVD transmission is well understood and evidence from the current and previous epidemics indicates that transmission can be interrupted by infection control measures. The central element of providing care to a patient suspected of Ebola is a three-step triage process: identify/isolate/inform. Once the diagnosis is confirmed, because of the potentially worse outcomes seen in pregnancy, specialized multidisciplinary care may be needed. In addition, especially in the obstetric setting, there is a high likelihood of exposure to a potentially deadly disease by health care workers. Therefore, these patients should be managed by anesthetic and obstetric providers in centers with expertise, protocols and training. Labor pain management, and the decision to proceed with cesarean delivery or other obstetric interventions will need to be considered on a case-by-case basis, weighing the risks and benefits to the mother, the fetus and the caregivers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| 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".