Bacterial agents and antibiotic resistance in febrile neutropaenia in Africa: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Febrile neutropaenia (FN) is an oncology emergency, but there is a paucity of data on it in Africa.Aim: This study aimed to review and aggregate data on FN in the context of antibiotic resistance.Methods: Published original articles between 1991 and 2024 were systematically searched in Google Scholar, PubMed, and African Journals Online databases (grey literature excluded). ‘Febrile neutropenia’ was combined by Boolean terms ‘OR’ and ‘AND’ with individual countries for the searched terms. Data aggregation on bacteria isolates and antibiotics was done using Microsoft Excel.Results: Of 16 637 articles retrieved, 15 (from nine countries) with 1216 non-duplicate isolates were included in the analyses after exclusion of irrelevant and duplicate articles. There were 57.0% (698/1225) Gram-positive and 43.3% (527/1225) Gram-negative bacteria. Aggregated resistance to antibiotics for Gram-positive bacteria was 71.8% (163/227), for ampicillin, 74.3% (226/304), for cefoxitin, 64.1% (25/39), and 54.0% (47/87) for oxacillin, while that of Gram-negative bacteria was 35.5% (184/519) for ciprofloxacin, 60.6% (168/277) for ceftriaxone, 65.9% (89/135) for cefuroxime, and 38.2% (153/401) for imipenem. Staphylococcus aureus had 68.8% (22/32) resistance to oxacillin/methicillin and 10% (1/10) resistance to vancomycin. Klebsiella spp. was 50% (9/18) resistant to quinolones, 75.9% (22/29) resistant to third-generation cephalosporins, and 25.0% (4/16) resistant to carbapenems, while Acinetobacter spp. was 85.7% (6/7) resistant to gentamycin.Conclusion: This review highlighted the paucity of data and the emergence of multidrug resistance in FN in Africa. There is a need for antibiotic-resistance surveillance and antibiotic stewardship to optimise therapy in FN in Africa.What this study adds: To the best of our knowledge, this is the first systematic review of FN in Africa in the context of available laboratory resources across the African regions.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".