Single simple question in axonal polyneuropathy
Bibliographic record
Abstract
INTRODUCTION/AIMS: The single simple question (SSQ), "What percentage of normal (0%-100%) do you feel regarding your disease?" has proven feasible and valid in assessing myasthenia gravis and a heterogeneous spectrum of neuropathies. This study explores the utility of the SSQ in axonal polyneuropathies (PNPs), encompassing diabetic neuropathy, and evaluates its responsiveness to scale changes. METHODS: A retrospective chart review of 150 patients with axonal PNP responding to the SSQ was performed. Patients underwent clinical and electrophysiological evaluations, and were evaluated by clinical and disability scales, including the Medical Research Council sum score, modified Toronto Clinical Neuropathy score (mTCNS), Overall Neuropathy Limitation Scale, and Rasch-built Overall Disability Scale (RODS). RESULTS: The SSQ total scores correlated moderately with both the RODS score (r = .59, p < .001) and the mTCNS symptom score (r = -.43, p < .001), maintaining significance after adjustment for multiple comparisons. Longitudinally, after adjusting for multiple comparisons, the change in mTCNS symptom score retained statistical significance (adjusted p = .048). The SSQ did not show any association with electrophysiological parameters or sensory symptoms, other than a lower score in those with pain (100% with SSQ <40%, 85% with SSQ 40%-70%, and 34% with SSQ >70%). DISCUSSION: The SSQ is a feasible, valid scale that may be utilized to assess and follow patients with length-dependent axonal PNPs. Given that the SSQ is not strongly associated with clinical and disability scales or electrophysiological findings, additional investigations are required for a comprehensive assessment of PNP.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
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".