Accuracy of C-reactive Protein and Procalcitonin for Diagnosing Bacterial Infections Among Subjects With Persistent Fever in the Tropics
Bibliographic record
Abstract
Background: In low-resource settings, inflammatory biomarkers can help identify patients with acute febrile illness who do not require antibiotics. Their use has not been studied in persistent fever (defined as fever lasting for ≥7 days at presentation). Methods: C-reactive protein (CRP) and procalcitonin (PCT) levels were measured in stored serum samples of patients with persistent fever prospectively enrolled in Cambodia, the Democratic Republic of Congo, Nepal, and Sudan. Diagnostic accuracy was assessed for identifying all bacterial infections and the subcategory of severe infections judged to require immediate antibiotics. Results: <.001). Sensitivity for overall and severe bacterial infections was 76.3% (469/615) and 88.2% (194/220) for CRP >10 mg/L, 62.4% (380/609) and 76.8% (169/220) for PCT >0.1 µg/L, and 30.5% (184/604) and 43.7% (94/215) for WBC >11 000/µL, respectively. Initial CRP level was <10 mg/L in 45% of the participants who received antibiotics at first presentation. Conclusions: In patients with persistent fever, CRP and PCT showed higher sensitivity for bacterial infections than WBC count, applying commonly used cutoffs for normal values. A normal CRP value excluded the vast majority of severe infections and could therefore assist in deciding whether to withhold empiric antibiotics after cautious clinical assessment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".