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Record W2593716909 · doi:10.3899/jrheum.161063

Increased Incidence of Gastrointestinal Side Effects in Patients Taking Hydroxychloroquine: A Brand-related Issue?

2017· letter· en· W2593716909 on OpenAlexvenueno aff
Amar Srinivasa, Sofia Tosounidou, Caroline Gordon

Bibliographic record

VenueThe Journal of Rheumatology · 2017
Typeletter
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHydroxychloroquineDysgeusiaRheumatologyAdverse effectIncidence (geometry)Internal medicineSide effect (computer science)Systemic lupus erythematosusDermatologyPediatricsDiseaseCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

To the Editor: Hydroxychloroquine (HCQ) is a well-established drug used in the treatment of systemic lupus erythematosus (SLE). Lupus UK, a national registered charity supporting people who have SLE, has received a number of recent reports from patients who are experiencing new adverse effects from HCQ after being established on the treatment without any initial adverse effects. These reports began in the first half of 2015 when Plaquenil (a brand name for HCQ) was discontinued1. At that time, many patients were switched to another brand of HCQ called Quinoric; subsequently, patients have frequently attributed side effects, mainly gastrointestinal (GI; such as dyspepsia, abdominal cramps, and dysgeusia), to this new brand of HCQ. We surveyed 128 patients attending the SLE clinic at City Hospital Birmingham, UK, over a 6-week period (June/July 2016) to examine whether there are brand-related differences in side effect prevalence for patients taking HCQ. There is a wealth of evidence that supports the use of HCQ in the treatment of SLE, … Address correspondence to A. Srinivasa, Department of Rheumatology, Birmingham City Hospital, Dudley Road, Birmingham B18 7QH, UK. E-mail: amarsrinivasa{at}nhs.net

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.283
Teacher spread0.271 · 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 teacher head, not a consensus.

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

Citations97
Published2017
Admission routes1
Has abstractyes

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