Potential usefulness of C-reactive protein and procalcitonin determination in patients admitted for neurological disorders in rural Democratic Republic of Congo
Bibliographic record
Abstract
In low-resource hospitals of central Africa, neurological disorders are frequent and etiologies very diverse. The difficulty to identify invasive bacterial infections in this setting results in major antibiotic overuse. Biomarkers such as C-reactive protein (CRP) and procalcitonin (PCT) may help discriminate these conditions. We retrospectively determined the concentrations of CRP and PCT in the sera of patients consecutively enrolled from 2012 to 2015 in an etiological study on neurological disorders at the rural hospital of Mosango, Democratic Republic of Congo. Invasive bacterial infection had been diagnosed by the demonstration of a bacterial pathogen in cerebrospinal fluid or blood cultures or the presence of radiological pneumonia. Sera of 313 (89.2%) and 317 (90.3%) of the 351 enrolled participants were available for determination of CRP and PCT concentrations respectively. Areas under the receiver operating characteristic curves for invasive bacterial infection, diagnosed in 19 tested cases, were 94.3% for CRP and 91.7% for PCT. No single case had a normal CRP concentration (<10 mg/L). Our data, although limited, suggest that CRP or PCT concentrations may help discriminate invasive bacterial infections in patients with neurological disorders in tropical settings and that normal CRP values could assist in withholding antibiotics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".