MétaCan
Menu
Back to cohort
Record W4280563495 · doi:10.3171/2022.2.jns212561

Idiopathic Parkinson’s disease and chronic pain in the era of deep brain stimulation: a systematic review and meta-analysis

2022· review· en· W4280563495 on OpenAlexaff
Oliver Flouty, Kazuaki Yamamoto, Jürgen Germann, Irene E. Harmsen, Hyun Ho Jung, Cletus Cheyuo, Ajmal Zemmar, Vanessa Milano, Can Sarica, Andrés M. Lozano

Bibliographic record

VenueJournal of neurosurgery · 2022
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDeep brain stimulationMeta-analysisChronic painPhysical medicine and rehabilitationParkinson's diseaseNeuroscienceDiseasePhysical therapyPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: Pain is the most common nonmotor symptom of Parkinson's disease (PD) and is often undertreated. Deep brain stimulation (DBS) effectively mitigates the motor symptoms of this multisystem neurodegenerative disease; however, its therapeutic effect on nonmotor symptoms, especially pain, remains inconclusive. While there is a critical need to help this large PD patient population, guidelines for managing this significant disease burden are absent. Herein, the authors systematically reviewed the literature and conducted a meta-analysis to study the influence of traditional (subthalamic nucleus [STN] and globus pallidus internus [GPi]) DBS on chronic pain in patients with PD. METHODS: The authors performed a systematic review of the literature and a meta-analysis following PRISMA guidelines. Risk of bias was assessed using the levels of evidence established by the Oxford Centre for Evidence-Based Medicine. Inclusion criteria were articles written in English, published in a peer-reviewed scholarly journal, and about studies conducting an intervention for PD-related pain in no fewer than 5 subjects. RESULTS: Twenty-six studies were identified and included in this meta-analysis. Significant interstudy heterogeneity was detected (Cochran's Q test p < 0.05), supporting the use of the random-effects model. The random-effects model estimated the effect size of DBS for the treatment of idiopathic pain as 1.31 (95% CI 0.84-1.79). The DBS-on intervention improved pain scores by 40% as compared to the control state (preoperative baseline or DBS off). CONCLUSIONS: The results indicated that traditional STN and GPi DBS can have a favorable impact on pain control and improve pain scores by 40% from baseline in PD patients experiencing chronic pain. Further trials are needed to identify the subtype of PD patients whose pain benefits from DBS and to identify the mechanisms by which DBS improves pain in PD patients.

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)0.019
metaresearch head score (Gemma)0.039
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 score0.020
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.039
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.046
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
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 head0.087
GPT teacher head0.337
Teacher spread0.249 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of neurosurgerySame topicNeurological disorders and treatmentsFrench-language works237,207