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Record W3011891022 · doi:10.1136/gpsych-2019-100112

Restless legs syndrome following the use of ziprasidone: a case report

2020· article· en· W3011891022 on OpenAlexaff
Cuizhen Zhu, Ran Bi, Yuliang Hu, Hui Zhou, Daomin Zhu, Brian Isaacson, Qingwei Li, Yezhe Lin

Bibliographic record

VenueGeneral Psychiatry · 2020
Typearticle
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsPublic Health Ontario
FundersShanghai Municipal Health Commission
KeywordsZiprasidoneRestless legs syndromeQuetiapineMedicineAntipsychoticPsychiatryAnesthesiaPsychologySchizophrenia (object-oriented programming)PediatricsInsomnia

Abstract

fetched live from OpenAlex

Restless legs syndrome (RLS) is a common sleep-related movement disorder characterised by an uncomfortable urge to move the legs that occurs during periods of inactivity. Although there have been many case reports on antipsychotic-induced RLS, ziprasidone has never been reported as a cause of RLS. We present a case of a female patient with schizophrenia who presented with symptoms of RLS following the administration of high doses of ziprasidone added to quetiapine and valproate. The patient's symptoms of RLS occurred following the administration and titration of ziprasidone to 160 mg, and were relieved upon reducing the dose to 120 mg/day. Other potential causative medications and differential diagnoses that could have caused similar symptoms were excluded. Clinicians should be aware of the potential for ziprasidone-induced RLS. Dopamine and serotonin interaction could be the mechanism underlying ziprasidone-induced RLS.

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 score0.007
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0070.004
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 head0.132
GPT teacher head0.356
Teacher spread0.224 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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