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Record W3216760836 · doi:10.3390/neurosci2040029

Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder Comorbid with Huntington’s Disease: A Case Report

2021· article· en· W3216760836 on OpenAlexaff
Clémence Noiseux, Jean‐Philippe Miron, Véronique Desbeaumes Jodoin, Tian Ren Chu, Sylvain Chouinard, Paul Lespérance

Bibliographic record

VenueNeuroSci · 2021
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsTranscranial magnetic stimulationMajor depressive disorderTolerabilityDeep transcranial magnetic stimulationNeurodegenerationHuntington's diseaseComorbidityPsychiatryMedicinePsychologyDiseaseNeuroscienceCognitionPhysical medicine and rehabilitationStimulationClinical psychologyInternal medicineAdverse effect

Abstract

fetched live from OpenAlex

Huntington’s disease (HD) is a rare genetic disorder resulting in progressive neurodegeneration leading to motor, cognitive and psychiatric symptoms. A high percentage of HD patients suffer from comorbid major depressive disorder (MDD). We are not aware of any literature on the use of repetitive transcranial magnetic stimulation (rTMS) for treating comorbid MDD in HD. We present the case of a 57-year-old man suffering from HD in which comorbid MDD was successfully treated with rTMS. Further work is required to better characterize the safety, tolerability and effectiveness of rTMS to treat comorbid MDD in HD.

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)0.002
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 score0.009
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0090.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.274
Teacher spread0.254 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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