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Record W3037423147 · doi:10.1371/journal.pone.0233913

Reduction in total leukocytes in malaria patients compared to febrile controls: A systematic review and meta-analysis

2020· review· en· W3037423147 on OpenAlexaboutno aff
Manas Kotepui, Kwuntida Uthaisar Kotepui, Giovanni De Jesus Milanez, Frederick Ramirez Masangkay

Bibliographic record

VenuePLoS ONE · 2020
Typereview
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsnot available
FundersWalailak University
KeywordsFunnel plotMeta-analysisPublication biasSystematic reviewMedicineStudy heterogeneityMalariaMEDLINEMeta-regressionPsychological interventionInternal medicineRandom effects modelPathologyBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0150.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.161
GPT teacher head0.335
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations23
Published2020
Admission routes1
Has abstractyes

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