Procalcitonin and C-Reactive Protein As Markers of Bacteremia in Patients With Febrile Neutropenia Who Receive Chemotherapy for Acute Leukemia: A Prospective Study From Nepal
Bibliographic record
Abstract
PURPOSE The purpose of this study was to evaluate the clinical significance of the biomarkers procalcitonin (PCT) and C-reactive protein (CRP) in patients with febrile neutropenia (FN) undergoing chemotherapy for acute leukemia. METHODS We conducted a prospective, observational study in patients who developed FN while undergoing chemotherapy for acute leukemia. PCT and CRP were obtained in patients who presented with FN. Blood cultures also were obtained. The primary goals were to evaluate the ability of PCT and CRP to predict bacteremia in patients with FN. The secondary goals were to assess the prognostic role of PCT and CRP and to assess the microbiologic profile and culture sensitivity patterns in the study population. RESULTS A total of 124 episodes of FN that involved 67 patients with acute leukemia occurred in the study. PCT was superior to CRP in the prediction of bacteremia. The median PCT level in the bacteremia group was 3.25 ng/mL compared with 0.51 ng/mL in the group without bacteremia ( P < .01). The median values of CRP in the bacteremia and without-bacteremia groups were 119.3 mg/L and 94.5 mg/L, respectively ( P = .07). There were no differences in median PCT and CRP in patients who died and those who improved. Of the 42 positive cultures, Gram-negative bacteremia was common (86%), and Escherichia coli was the most frequent organism isolated. Carbapenem resistance was seen in 39% of positive cultures. CONCLUSION PCT is an effective biomarker to predict bacteremia in patients with FN undergoing chemotherapy for acute leukemia.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".