Association of social support with quality of life in patients with polyneuropathy
Bibliographic record
Abstract
The aim of this study was to examine the impact of social support on quality of life (QoL) in patients with polyneuropathy. One hundred and fifty-four patients with polyneuropathy were enrolled from a neuromuscular clinic. The QoL Instrument and the Medical Outcome Study-Social Support Survey (MOS-SSS) were used to assess QoL and social support, respectively. Disease severity and clinical factors were also assessed. Neuropathy patients had a lower QoL compared to a previously published normative sample (p < 0.0001) and an MOS-SSS comparable to other patients with chronic disease. Social support correlated weakly with the self esteem and emotional well being mental health dimensions (rs :0.20-0.38) but not the physical health QoL (PH-QoL) domains. Physical and mental QoL also correlated significantly with presence of pain (rs : -0.39 and -0.42, respectively) and number of autonomic symptoms (rs : -0.39 and -0.30, respectively). Social support independently predicts MH-QoL when controlling for age, gender, pain, and the Toronto Clinical Neuropathy Score (TCNS; p < 0.0001). TCNS and gender are independently related to PH-QoL (p < 0.05). This study demonstrates that improved social support serves as an independent predictor of MH-QoL when controlling for age, gender, pain, and severity of neuropathy. Future studies examining the effects of improving social support on QoL in patients with polyneuropathy are recommended.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".