A retrospective cohort study to predict severe dengue in Honduran patients
Bibliographic record
Abstract
BACKGROUND: An important challenge in the identification of dengue is how to predict which patients will go on to experience severe illness, which is typically characterized by fever, thrombocytopenia, haemorrhagic manifestations, and plasma leakage. Accurate prediction could result in the appropriate hospital triage of high risk patients. The objective of this study was to identify clinical factors observed within the first 24 h of hospital admission that could predict subsequent severe dengue. METHODS: We conducted a retrospective cohort study of 320 patients with febrile illness who had confirmation of dengue within one week of admission, using data from the 2009-2010 Honduras Epidemiological Survey for Dengue. The outcome measure was plasma leakage defined using hemoconcentration ≥15% as determined by serial hematocrit testing. We conducted univariable analysis and multivariable logistic regression analysis to construct a predictive model for severe dengue. RESULTS: at admission, OR 3.02, 95% CI 1.42 to 6.42, were significantly associated with plasma leakage, while the presence of petechiae, OR 0.24 95% CI 0.080 to 0.73, and headache, OR 0.38, 95% CI 0.15 to 0.95, were negatively associated with leakage. Using an estimated probability of 7% as a threshold for a person being considered a severe case correctly predicted 26 of the 34 severe cases (sensitivity 76.4%) and 201 of the 286 non-severe cases (specificity of 70.3%) for a percentage correctly classified of 70.9%. CONCLUSION: We identified signs and symptoms that can correctly identify a majority of patients who eventually develop severe dengue in Honduras. It will be important to further refine our models and validate them in other populations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".