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Record W4220702027 · doi:10.1097/wnp.0000000000000921

Evaluation of the Cutaneous Silent Period in a Healthy Brazilian Population

2022· article· en· W4220702027 on OpenAlexaff
N Cunha, Talyta Grippe, Raphael Boëchat‐Barros, Rubens Nelson Morato Fernandez, Pedro Renato de Paula Brandão, Robert Chen, Francisco Cardoso

Bibliographic record

VenueJournal of Clinical Neurophysiology · 2022
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsKrembil Foundation
Fundersnot available
KeywordsMedicinePercentileLatency (audio)Silent periodReproducibilityAudiologyNerve conductionPopulationSurgeryInternal medicineStimulationMathematicsStatistics

Abstract

fetched live from OpenAlex

PURPOSE: To establish cutaneous silent period (CSP) normative values and investigate the variables that may influence them. METHODS: We tested 41 healthy subjects. All subjects underwent nerve conduction studies, and we evaluated the CSP in both arms. RESULTS: Four subjects did not have CSP and were excluded. The analyses were performed in the healthy group composed of 23 women and 14 men, with a mean age of 35 (range, 19-64) years. The CSP median duration was 23.2 milliseconds (ms), with 2 to 98th percentile at 11.3 and 48.7 ms. The median onset latency was 87.9 (range, 72.9-109) ms, and the median end latency was 112 (range, 93.8-138) ms. The CSP onset latency positively correlated with height, whereas CSP end latency and duration were weakly but significantly associated with age. Some measurements of ulnar nerve conduction study also correlated with CSP measures. The interrater coefficients for the primary measures of onset and end latency demonstrates the reproducibility of the method. CONCLUSIONS: The CSP with the fifth digit stimulation and recording from the abductor digiti minimi muscle is a valid diagnostic tool that can be used in clinical practice.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.075
GPT teacher head0.426
Teacher spread0.351 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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