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Record W1980181887 · doi:10.1177/1352458506070659

Identifying reliable change in tactile temporal thresholds in multiple sclerosis: test-retest reliability

2006· article· en· W1980181887 on OpenAlexafffund
Lenora N. Brown, L Metz, Michael Eliasziw

Bibliographic record

VenueMultiple Sclerosis Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReliability (semiconductor)Multiple sclerosisAudiologySensory thresholdPsychologyMedicinePhysical medicine and rehabilitationCognitive psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Tactile temporal thresholds are typically significantly higher (ie, prolonged) in multiple sclerosis (MS) patients when compared to controls and increase significantly during relapses, probably reflecting integrity of conduction across a portion of the corpus callosum. As part of an ongoing validation study of tactile temporal thresholds, the test-retest reliability of these thresholds was examined in patients with MS. METHODS: Tactile temporal thresholds were measured in 61 MS patients during two separate test sessions within three weeks. Test-retest reliability and the standard error of measurement were calculated. The threshold of change in tactile temporal thresholds in MS patients that would correspond to real change beyond measurement error with 95% certainty was also calculated. RESULTS: The test-retest reliability of this measure of tactile temporal thresholds was 0.93. The threshold indicating change beyond chance or measurement error with 95% certainty was 19 ms. CONCLUSIONS: This measure of tactile temporal thresholds has excellent test-retest reliability and a change of greater than 19 ms is highly likely to represent real change. This measure is promising as a precise, reliable outcome measure in MS.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.003
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 head0.181
GPT teacher head0.323
Teacher spread0.142 · 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.

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

Citations5
Published2006
Admission routes2
Has abstractyes

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