Clinical features and risk factors for death in acute undifferentiated fever: A prospective observational study in rural community hospitals in six states of India
Bibliographic record
Abstract
BACKGROUND: Acute undifferentiated fever (AUF) ranges from self-limiting illness to life-threatening infections, such as sepsis, malaria, dengue, leptospirosis and rickettsioses. Similar clinical presentation challenges the clinical management. This study describes risk factors for death in patients hospitalized with AUF in India. METHODS: Patients aged ≥5 y admitted with fever for 2-14 d without localizing signs were included in a prospective observational study at seven hospitals in India during 2011-2012. Predictors identified by univariate analysis were analyzed by multivariate logistic regression for survival analysis. RESULTS: Mortality was 2.4% (37/1521) and 46.9% (15/32) died within 2 d. History of heart disease (p=0.013), steroid use (p=0.011), altered consciousness (p<0.0001), bleeding (p<0.0001), oliguria (p=0.020) and breathlessness (p=0.015) were predictors of death, as were reduced Glasgow coma score (p=0.005), low urinary output (p=0.004), abnormal breathing (p=0.006), abdominal tenderness (p=0.023), leucocytosis (p<0.0001) and thrombocytopenia (p=0.001) at admission. Etiology was identified in 48.6% (18/37) of fatal cases. CONCLUSIONS: Bleeding, cerebral dysfunction, respiratory failure and oliguria at admission, suggestive of severe organ failure secondary to systemic infection, were predictors of death. Almost half of the patients who died, died shortly after admission, which, together with organ failure, suggests that delay in hospitalization and, consequently, delayed treatment, contribute to death from AUF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".