Evidence map›Paper›PMID 41313026›Full record

Observational studyMicrobiology spectrum2026

A novel diagnostic strategy of differential diagnosis of tuberculous meningitis and non-tuberculous meningitis: a retrospective observational cohort study.

Qingwen Lin, Wenhua Fang, Kengna Fan, Weiqing Zhang, Xiaxia Qiu, Minjie Tang, Qi Wang, Huangcheng Shangguan, Qishui Ou, Xiaofeng Liu

Abstract readObservational Study
In one paragraph

Observational study in Microbiology spectrum, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

10 authors.

Qingwen Lin *Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Wenhua Fang *Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Kengna Fan *Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Weiqing ZhangDepartment of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Xiaxia QiuDepartment of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Minjie TangDepartment of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Qi WangDepartment of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Huangcheng ShangguanDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Qishui OuDepartment of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-9923-3212
Xiaofeng LiuDepartment of Laboratory Medicine, Geriatric Hospital Affiliated with Wuhan University of Science and Technology, Wuhan, China.ORCID 0000-0002-8545-3951

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculous meningitis (TBM) leads to severe disability and mortality rates, making early diagnosis critical. However, it is challenging to distinguish it from other common forms of non-tuberculous meningitis (non-TBM), including bacterial meningitis, cryptococcal meningitis, and viral meningitis. This study aims to construct a diagnostic model between TBM and non-TBM. A total of 543 patients were enrolled, and 405 subjects remained and were subsequently divided into a training set and a validation set. Basic information, laboratory results, and imaging results of patients were collected, and R4.1.0 was used to construct and validate the diagnostic model. Subsequently, 30 patients were recruited as an independent validation cohort to verify the diagnostic efficacy of the model further. Finally, 10 patients with suspected TBM were prospectively observed, and the model was applied for diagnosis, with results compared to the final clinical diagnosis. The differential model of TBM and non-TBM was composed of the systemic symptoms of tuberculosis, altered consciousness, neurological deficits, meningeal irritation, cerebrospinal fluid protein, positive T-cell spot test for tuberculosis infection, and C-reactive protein. The areas under the receiver operating characteristic curve for the model in the training and validation sets were 0.872 (95% confidence interval [CI] = 0.833-0.913) and 0.844 (95% CI = 0.751-0.937), respectively. Furthermore, the validation cohort also shows good diagnostic performance with a sensitivity and specificity of 88.9% and 85.7%, respectively. Notably, 9 out of 10 patients had diagnoses consistent with model predictions. A novel diagnostic model was developed and validated using common clinical indicators and laboratory results to distinguish between TBM and non-TBM effectively.IMPORTANCETuberculous meningitis is a serious disease. Currently, there is no effective way to perform early differential diagnosis, particularly in resource-limited settings. This article presents a new, simple method.

Indexed as

Tuberculosis, MeningealAdultAgedDiagnosis, DifferentialFemaleHumansMaleMeningitis, BacterialMeningitis, CryptococcalMiddle AgedRetrospective StudiesROC CurveSensitivity and SpecificityYoung Adultdiagnosismodelnon-tuberculous meningitistuberculous meningitis

Identifiers

PMID41313026
PMCPMC12772306

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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.