MétaCan
Menu
← Back to cohort
Record W4206895649

Gray Matter Abnormalities in Patients with Chronic Primary Pain: A Coordinate-Based Meta-Analysis.

2022· article· en· W4206895649 on OpenAlexaboutno aff
Zuxing Wang, Minlan Yuan, Jun Xiao, Xiaoyun Guo, Yikai Dou, Fugui Jiang, Wenjiao Min, Bo Zhou

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGray (unit)Magnetic resonance imagingMeta-analysisAnterior cingulate cortexVoxel-based morphometryCochrane LibraryNuclear medicineWhite matterInternal medicineRadiologyPsychiatryCognition
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Many structural magnetic resonance imaging (MRI) studies have used voxel-based morphometry (VBM) to identify gray matter abnormalities in patients with chronic primary pain (CPP), but the findings have been inconsistent. OBJECTIVES: To identify (a) gray matter differences between CPP patients or female patients and healthy individuals and (b) the effects of symptom duration and pain scores on gray matter. STUDY DESIGN: We conducted a meta-analysis. METHODS: VBM studies in PubMed, Cochrane Library, and Google Scholar, from November 2005 to June 2020, were thoroughly collected and carefully reviewed. Manual searches were performed using title and citation information. Gray matter VBM study comparing adult patients (18-65 years) with CPP to healthy controls was reviewed, and results, presented in Talairach or Montreal Neurological Institute coordinates, were included. The t value, peak coordinates, and basic clinical information of each study were reported in detail. Anisotropic effect-size signed differential mapping was used for voxel-based meta-analyses. RESULTS: Patients with CPP had decreased gray matter in the left anterior cingulate (z value = 2.950, P < 0.001), right median cingulate (z value = 1.858, P = 0.001), and the insula bilaterally (left: z value = 2.441, P < 0.001; right: z value = 2.113, P < 0.001 ), and increased gray matter in the right striatum (z value = 1.194, P < 0.001). Subgroup meta-analysis showed female patients with CPP also had decreased gray matter in the left anterior cingulate gyrus (z value = 2.622, P < 0.001). Meta-regression analyses revealed that pain symptom duration was positively associated with a large right brain region (z value = 2.110, P < 0.001), a negative association between pain symptom duration and gray matter was found in the right anterior cingulate (z value = 1.969, P < 0.001) and right middle frontal gyrus (z value = 1.849, P < 0.001). LIMITATIONS: Due to the lack of data from male patients, we were unable to perform a male subgroup analysis; therefore, we cannot thoroughly explore the difference in CPP from the perspective of gender. CONCLUSION: We identified gray matter changes in CPP patients and female patients, as well as a close relationship between CPP and mental disorders. With the chronicity of pain leads to changes in relevant brain regions, which makes treatment more challenging and may have synergistic effects with affective disorders. More prospective longitudinal structural MRI studies of CPP examining the associations between those variables and gray matter in a larger population should be conducted. Additional prospective longitudinal structural MRI studies of CPP with larger sample sizes to confirm the relationships between these variables and gray matter are needed as well as gender differences of CPP in brain structure and function.

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)0.011
metaresearch head score (Gemma)0.016
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: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.030
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.201
Teacher spread0.181 · 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
GenreEmpirical

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

Citations22
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→Same topicPain Mechanisms and Treatments→French-language works237,207→