MétaCan
Menu
Back to cohort
Record W2980362927 · doi:10.1002/mds.27876

Parkinsonism Associated with Gabapentinoid Drugs: A Pharmacoepidemiologic Study

2019· article· en· W2980362927 on OpenAlexaff
Tatiana Pacheco, François Montastruc, Vanessa Rousseau, Leila Chebane, Maryse Lapeyre‐Mestre, Christel Renoux, Jean‐Louis Montastruc

Bibliographic record

VenueMovement Disorders · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsPregabalinDuloxetineGabapentinParkinsonismMedicineOdds ratioPsychiatryInternal medicineAnesthesiaDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Use of gabapentinoids is increasing. Following recent case reports, we investigated a putative risk of parkinsonism with pregabalin or gabapentin. METHODS: A disproportionality analysis of 5,653,547 individual case safety reports in the World Health Organization individual case safety report database, VigiBase, compared all patients with parkinsonism who were receiving gabapentinoids with other patients. Results are shown as reporting odds ratios and the information component, an indicator of disproportionate Bayesian reporting. Sensitivity analyses included comparisons with drugs used for similar indications (amitriptyline, duloxetine) and exclusion of drugs that induce parkinsonism. RESULTS: Among 5,653,547 reports, 4925 parkinsonism reports were found with pregabalin and 4881 with gabapentin. Gabapentin and pregabalin were associated with increased reporting odds ratio (2.16 [2.10-2.23], 2.43 [2.36-2.50]). Similar trends were found using information components after excluding drugs that induce parkinsonism and for pregabalin compared with amitriptyline or duloxetine. CONCLUSIONS: This study found that gabapentinoids (particularly pregabalin) can be associated with parkinsonism. © 2019 International Parkinson and Movement Disorder Society.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.062
GPT teacher head0.406
Teacher spread0.344 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations14
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueMovement DisordersSame topicPharmacovigilance and Adverse Drug ReactionsFrench-language works237,207