Factors Associated with Marburg Hemorrhagic Fever: Analysis of Patient Data from Uige, Angola
Bibliographic record
Abstract
BACKGROUND: Reliable on-site polymerase chain reaction (PCR) testing for Marburg hemorrhagic fever (MHF) is not always available. Therefore, clinicians triage patients on the basis of presenting symptoms and contact history. Using patient data collected in Uige, Angola, in 2005, we assessed the sensitivity and specificity of these factors to evaluate the validity of World Health Organization (WHO)-recommended case definitions for MHF. METHODS: Multivariable logistic regression was used to identify independent predictors of PCR confirmation of MHF. A data-derived algorithm was developed to obtain new MHF case definitions with improved sensitivity and specificity. RESULTS: A MHF case definition comprising (1) an epidemiological link or (2) the combination of myalgia or arthralgia and any hemorrhage could potentially serve as an alternative to current case definitions. Our data-derived case definitions maintained the sensitivity and improved the specificity of current WHO-recommended case definitions. CONCLUSIONS: Continued efforts to improve clinical documentation during filovirus outbreaks would aid in the refinement of case definitions and facilitate outbreak control.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".