Evidence map›Paper›PMID 41890234›Full record

ArticleInfection and drug resistance2026

Development and Validation of a Novel Predictive Model and Simplified Clinical Indicator for Mortality in Severe Fever with Thrombocytopenia Syndrome Patients.

Debao Shi, Yaping Pan

Abstract read
In one paragraph

Article in Infection and drug resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Debao ShiDepartment of Clinical Laboratory, The First Affliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.
Yaping PanDepartment of Clinical Laboratory, The First Affliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.ORCID 0009-0000-9807-4152

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Severe fever with thrombocytopenia syndrome (SFTS) is a highly fatal infectious disease endemic in rural areas, underscoring the need for early prognostic risk stratification. Patients and Methods: This study aimed to develop and validate a prognostic model using readily available clinical indicators. We conducted a retrospective analysis of 260 SFTS patients (mortality rate 19.6%), integrating epidemiological characteristics, clinical symptoms and laboratory variables through a hybrid multi-model feature screening approach incorporating univariate Cox, LASSO, Random Forest, and XGBoost algorithms. Results: Four core risk factors for mortality were identified: age ≥ 56 years, platelet count ≤ 44 × 10 Conclusion: The developed model and AER index provide valuable tools for early identification of high-risk SFTS patients, facilitating improved clinical management and resource allocation.

Indexed as

activated partial thromboplastin time-to-estimated glomerular filtration rate ratioacute kidney injurymachine learning algorithmspredictive modelssevere fever with thrombocytopenia syndrome

Identifiers

PMID41890234
PMCPMC13016124

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.