Reduction in total leukocytes in malaria patients compared to febrile controls: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Leukocyte alterations are a common hematological alteration among malaria patients. Objectives This systematic review and meta-analysis aimed to provide data and evidence comparing alterations in total leukocyte counts in malaria patients compared to febrile/healthy subjects at baseline before treatment. A systematic review was conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for reporting systematic reviews and meta-analyses. Data sources Web of Science (ISI), Scopus, and Medline. Study eligibility criteria, participants, and interventions All published articles reporting a total leukocyte count of patients infected with malaria, non-malaria (febrile or healthy group) at baseline before treatment before August 27, 2019, were retrieved, and data were extracted by two main reviewers independently. Study appraisal and synthesis methods We used a forest plot, heterogeneity test (Cochran's Q), and the degree of heterogeneity (I2) to test whether the included studies were heterogeneous. The quality of the included studies was determined by a quality assessment guide based on the quality assessment tool developed by the Newcastle-Ottawa Scale (NOS). Cochran's Q (Chi-square) and Moran's I2 were used to evaluate heterogeneity. Meta-regression using STATA software was conducted to find the source of heterogeneity. A funnel plot with Egger's test was used to examine the significance of publication bias among the included studies. The mean differences were estimated using a random-effects model. Results Out of the 2,261 articles screened, 29 articles were included in this systematic review and meta-analysis. The heterogeneity test indicated that there was heterogeneity among the included studies with no publication bias. The meta-analysis demonstrated that the total leukocyte count was significantly lower in patients with malaria (n = 4,619) than in those without malaria (n = 10,056) (Z = 4.0, P-value < 0.00001, mean difference = -1.38, 95% CI = -2.06-(-0.71)). Leukocyte differential alterations, low lymphocyte counts (P-value <0.0001, mean difference = -1.03, 95% CI = -1.53-(-0.53)) and a high NL ratio were found in the malaria group (n = 1,579) compared to the non-malaria group (n = 4,991) (P-value <0.0001, mean difference = 0.6, 95% CI = 0.32–0.88). The subgroup analysis indicated that there was a significantly lower total leukocyte count in the malaria group (n = 3,545) than in the febrile group (n = 8,947) (Z = 1.33, P-value < 0.0001, mean difference = -1.76, 95% CI = -2.56-(-0.96)), but no significant difference was found between the malaria group (n = 1,232) and the healthy group (n = 1,679) (P-value > 0.05). Limitations As the specific diagnoses in the febrile groups were not reported in the included studies so that the results of the present study need to be carefully interpreted. Conclusions and implications of key findings This systematic review demonstrated that the total leukocyte count was affected by malarial infection at baseline despite the heterogeneity of the included studies. Future work must aim to understand the treatment-related total leukocyte reduction during follow-up or post-treatment outcomes in malaria-endemic settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.048 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".