Bibliographic record
Abstract
Peripheral neuropathy (PN) is a common impairment which may impact upon quality of life (QoL). Neuropathic pain (NeP) occurs in up to 50% of patients with PN. We hypothesized that disability and impaired quality of life resulting from PN is primarily associated with presence of NeP. Our aim was to determine using prospectively identified PN patients presenting to a tertiary care neuromuscular clinic if presence of NeP (PN+NeP) had greater impact upon QoL than with absence of NeP (PN-NeP). A second aim was to identify if QoL varied based upon etiology of PN. We analyzed neuropathy severity (Toronto Clinical Neuropathy Score (TCSS)), pain quantity and quality (Visual Analogue Scale (VAS) pain score, Brief Pain Inventory (BPI)), QoL and health status measures (EuroQol Instrument 5 Domains (EQ-5D), Medical Outcomes Sleep Study Scale (MOSSS), Hospital Anxiety and Depression Scale (HADS), Short Form 36 Health Survey (SF-36)) and Health Assessment Questionnaire (HAQ) to determine impact of NeP. Although both cohorts were epidemiologically similar and had similar severity of PN, PN+NeP patients had considerably greater impairment for QoL, sleep efficacy, and features of anxiety and depression, leading to substantially greater health care resources utilization when compared to PN-NeP patients. The magnitude of NeP severity was the only explaining variable for increased impact upon QoL measures and diminishing overall wellbeing. Our results confirm that NeP is a primary indicator for worsening QoL and diminished overall wellbeing in PN patients. The etiology of PN did not influence levels of NeP-related compromise of QoL. Further studies are needed to determine optimal methods for management of PN+NeP patients subjected to a significant physiological, psychological and functional burden.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".