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Low-frequency repetitive transcranial magnetic stimulation for treatment of levodopa-induced dyskinesias

2007· article· en· W2129960864 on OpenAlexaff
Aparna Wagle Shukla, M J Angel, Cindy Zadikoff, M. Enjati, Carolyn Gunraj, Anthony E. Lang, Robert Chen

Bibliographic record

VenueNeurology · 2007
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity Health Network
FundersAmerican Cancer Society
KeywordsTranscranial magnetic stimulationLevodopaMedicineStimulationPhysical medicine and rehabilitationNeuroscienceAudiologyPsychologyInternal medicineParkinson's diseaseDisease

Abstract

fetched live from OpenAlex

Levodopa-induced dyskinesia (LID) is a major source of disability in Parkinson disease (PD) and functional imaging studies suggest that it may be related to overactivity of the motor cortex.1 Low- frequency repetitive transcranial magnetic stimulation (rTMS) at about 1 Hz decreases excitability of the stimulated area.2 A single 15-minute session of 1 Hz rTMS to the supplementary motor area reduced LID for up to 15 minutes.3 We hypothesize that longer sessions4 such as a 2-week course of 1 Hz rTMS to primary motor cortex will produce longer lasting reduction of LID. We studied six patients with PD (table E-1 on the Neurology Web site at www.neurology.org). Inclusion criteria were 1) stable medication dose for 4 weeks, and 2) LID >25% of waking hours (item 32 of Unified PD Rating Scale [UPDRS] ≥ 2) and bothersome (item 33 ≥ 2). Exclusion criteria were previous PD surgery and contraindications to rTMS. Patients took regular medications during the study except on days of levodopa challenge test. Informed consent and Ethics Board approval were obtained. Patients received 10 days of …

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.050
GPT teacher head0.306
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations69
Published2007
Admission routes1
Has abstractyes

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