Identifying reliable change in tactile temporal thresholds in multiple sclerosis: test-retest reliability
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".