The Contribution of Ebola Viral Load at Admission and Other Patient Characteristics to Mortality in a Médecins Sans Frontières Ebola Case Management Centre, Kailahun, Sierra Leone, June–October 2014
Bibliographic record
Abstract
This paper describes patient characteristics, including Ebola viral load, associated with mortality in a Médecins Sans Frontières Ebola case management centre (CMC).Out of 780 admissions between June and October 2014, 525 (67%) were positive for Ebola with a known outcome. The crude mortality rate was 51% (270/525). Ebola viral load (whole-blood sample) data were available on 76% (397/525) of patients. Univariate analysis indicated viral load at admission, age, symptom duration prior to admission, and distance traveled to the CMC were associated with mortality (P < .05). The multivariable model predicted mortality in those with a viral load at admission greater than 10 million copies per milliliter (P < .05, odds ratio >10), aged ≥ 50 years (P = .08, odds ratio = 2) and symptom duration prior to admission less than 5 days (P = .14). The presence of confusion, diarrhea, and conjunctivitis were significantly higher (P < .05) in Ebola patients who died.These findings highlight the importance viral load at admission has on mortality outcomes and could be used to cohort cases with viral loads greater than 10 million copies into dedicated wards with more intensive medical support to further reduce mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".