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Record W2945783379 · doi:10.3389/fneur.2019.00553

Colchicine Myopathy: A Case Series Including Muscle MRI and ABCB1 Polymorphism Data

2019· article· en· W2945783379 on OpenAlexafffund
Mehul Gupta, Ana Nikolić, Denise Ng, Kristina Martens, Hamid Ebadi, Sameer Chhibber, Gerald Pfeffer

Bibliographic record

VenueFrontiers in Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersCumming School of Medicine, University of CalgaryAlberta Innovates
KeywordsColchicineMyopathyMedicineGoutInternal medicineAdverse effectGenotypeGastroenterologyPharmacologyBiologyGenetics

Abstract

fetched live from OpenAlex

Colchicine is a medication most commonly used in the treatment of gout and familial mediterannean fever. A rare complication of therapy is toxicity causing proximal myopathy and polyneuropathy. Colchicine myopathy has been associated with the coadministration of other medications with colchicine, such as statins or tacrolimus, and is more common in patients with renal impairment. Otherwise, it is unclear which patients are at greatest risk of developing this adverse drug reaction. ABCB1 is important to the metabolism of colchicine, so we speculated that it was possible that colchicine myopathy patients may have a particular genotype that is associated with this side effect. We describe two cases of colchicine myopathy which occurred with co-administration of rosuvastatin. From one case, we present the first published data on muscle MRI in this condition. We additionally present an analysis of four genetic polymorphisms in ABCB1 and transcript levels in muscle tissue, and demonstrate the descriptive finding of reduced ABCB1 transcript levels in the colchicine myopathy 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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.260
Teacher spread0.238 · 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 designCase report
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

Citations21
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in NeurologySame topicGout, Hyperuricemia, Uric AcidFrench-language works237,207