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Record W4402462549 · doi:10.1016/j.brs.2024.09.004

Strategies to mitigate scalp discomfort during repetitive transcranial magnetic stimulation

2024· letter· en· W4402462549 on OpenAlexfundno aff
Jennifer I. Lissemore, Derrick Matthew Buchanan, Jean-Marie Batail, Irakli Kaloiani, Clive Veerapal, Gregory L. Sahlem, Nolan Williams

Bibliographic record

VenueBrain stimulation · 2024
Typeletter
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsTranscranial magnetic stimulationScalpMedicinePhysical medicine and rehabilitationStimulationNeurosciencePsychologySurgery

Abstract

fetched live from OpenAlex

While repetitive transcranial magnetic stimulation (rTMS) is a safe and well-tolerated treatment for various psychiatric conditions, up to 40 % of patients experience scalp discomfort or pain during stimulation [1,2]. Discomfort at the site of stimulation can make it difficult for some patients to reach their target dose, and has been associated with non-response to treatment [3,4]. Effective, feasible solutions to alleviate pain at the stimulation site could enhance tolerability and treatment outcomes, yet there is a shortage of empirical data on methods to mitigate discomfort during stimulation.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.028
GPT teacher head0.289
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2024
Admission routes1
Has abstractyes

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