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Record W4391885025 · doi:10.2196/49285

Integrated Network Analysis of Symptom Clusters Across Monkeypox Epidemics From 1970 to 2023: Systematic Review and Meta-Analysis

2024· review· en· W4391885025 on OpenAlexvenueno aff
Shu Su, Meng Jia, Yingni Yu, Hu Li, Wenwei Yin, Yi Lu, Rongzhong Huang, Rong Xiang, Huizhe Huang, Peng Hu

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsnot available
FundersCentre Scientifique et Technique du BâtimentNatural Science Foundation of ChongqingChongqing Medical UniversityNational Natural Science Foundation of China
KeywordsMedicineMeta-analysisRashMonkeypoxPediatricsOutbreakInternal medicineDemographyVirology

Abstract

fetched live from OpenAlex

BACKGROUND: The worldwide spread of monkeypox (mpox) has witnessed a significant increase, particularly in nonendemic countries. OBJECTIVE: We aimed to investigate the changing clinical symptoms associated with mpox from 1970 to 2023 and explore their interrelations. METHODS: In this systematic review and meta-analysis, 3 electronic databases were searched for English peer-reviewed studies conducted from January 1970 to April 2023 that reported any symptoms among confirmed mpox cases. We categorized the mpox epidemics into 3 periods: 1970-2002 (period 1, within the African region), 2003-2021(period 2, epidemics outside Africa), and 2022-2023 (period 3, worldwide outbreak). Following PRISMA guidelines, a meta-analysis was performed to estimate the pooled prevalence for each symptom. The correlation among symptoms was analyzed and visualized using network analysis. RESULTS: The meta-analysis included 61 studies that reported 21 symptoms in 720 patients from period 1, 39 symptoms in 1756 patients from period 2, and 37 symptoms in 12,277 patients from period 3. The most common symptom among patients from all 3 periods was rash (period 1: 92.6%, 95% CI 78.2%-100%; period 2: 100%, 95% CI 99.9%-100%; and period 3: 94.8%, 95% CI 90.9%-98.8%), followed by lymphadenopathy (period 1: 59.8%, 95% CI 50.3%-69.2%; period 2: 74.1%, 95% CI 64.2%-84.1%; and period 3: 61.1%, 95% CI 54.2%-68.1%). Fever (99%, 95% CI 97%-100%), enlarged lymph nodes (80.5%, 95% CI 75.4%-85.0%), and headache (69.1%, 95% CI 4%-100%) were the main symptoms in period 1, with a significant decrease in period 3: 37.9%, 31.2%, and 28.7%, respectively. Chills/rigors (73.3%, 95% CI 60.9%-85.7%), fatigue (68.2%, 95% CI 51.6%-84.8%), and dysphagia/swallowing difficulty (61.2%, 95% CI 10.5%-100%) emerged as primary new symptoms in period 2 and decreased significantly in period 3. Most other symptoms remained unchanged or decreased in period 3 compared to the former 2 periods. Nausea/vomiting had the highest degree of correlation (with 13 symptoms) and was highly positively correlated with lymphadenopathy (r=0.908) and conjunctivitis (r=0.900) in period 2. In contrast, rash and headache were 2 symptoms with the highest degree of correlation (with 21 and 21 symptoms, respectively) in period 3 and were highly positively correlated with fever (r=0.918 and 0.789, respectively). CONCLUSIONS: The manifestation of symptoms in patients with mpox has become more diverse, leading to an increase in their correlation. Although the prevalence of rash remains steady, other symptoms have decreased. It is necessary to surveil the evolving nature of mpox and the consequential changes in clinical characteristics. Epidemic countries may shift their focus on the potential association among symptoms and the high synergy risk. TRIAL REGISTRATION: PROSPERO Registration: CRD42023403282; http://tinyurl.com/yruuas5n.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.043
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0190.061
Bibliometrics0.0120.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.406
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2024
Admission routes1
Has abstractyes

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