Prevalence of Neuropathic Pain and Patient-Reported Outcomes in Korean Adults with Chronic Low Back Pain Resulting from Neuropathic Low Back Pain
Bibliographic record
Abstract
<sec><title>Study Design</title><p>A noninterventional, multicenter, cross-sectional study.</p></sec><sec><title>Purpose</title><p>We investigated the prevalence of neuropathic pain (NP) and patient-reported outcomes (PROs) of the quality of life (QoL) and functional disability in Korean adults with chronic low back pain (CLBP).</p></sec><sec><title>Overview of Literature</title><p>Among patients with CLBP, 20%–55% had NP.</p></sec><sec><title>Methods</title><p>Patients older than 20 years with CLBP lasting for longer than three months, with a visual analog scale (VAS) pain score higher than four, and with pain medications being used for at least four weeks before enrollment were recruited from 27 general hospitals between December 2014 and May 2015. Medical chart reviews were performed to collect demographic/clinical features and diagnosis of NP (douleur neuropathique 4, DN4). The QoL (EuroQoL 5-dimension, EQ-5D; EQ-VAS) and functional disability (Quebec Back Pain Disability Scale, QBPDS) were determined through patient surveys. Multiple linear regression analyses were performed to compare PROs between the NP (DN4≥4) and non-NP (DN4&lt;4) groups.</p></sec><sec><title>Results</title><p>A total of 1,200 patients (females: 65.7%; mean age: 63.4±13.0 years) were enrolled. The mean scores of EQ-5D, EQ-VAS, and QBPDS were 0.5±0.3, 55.7±19.4, and 40.4±21.1, respectively. Among all patients, 492 (41.0%; 95% confidence interval, 38.2%–43.8%) suffered from NP. The prevalence of NP was higher in male patients (46.8%; <italic>p</italic>&lt;0.01), in patients who had pain based on radiological and neurological findings (59.0%; <italic>p</italic>&lt;0.01), and in patients who had severe pain (49.0%; <italic>p</italic>&lt;0.01). There were significant mean differences in EQ-5D (NP group vs. non-NP group: 0.4±0.3 vs. 0.5±0.3; <italic>p</italic>&lt;0.01) and QBPDS (NP group vs. non-NP group: 45.8±21.2 vs. 36.3±20.2; <italic>p</italic>&lt;0.01) scores. In the multiple linear regression, patients with NP showed lower EQ-5D (β=−0.1; <italic>p</italic>&lt;0.01) and higher QBPDS (β=7.0; <italic>p</italic>&lt;0.01) scores than those without NP.</p></sec><sec><title>Conclusions</title><p>NP was highly prevalent in Korean patients with CLBP. Patients with CLBP having NP had a lower QoL and more severe dysfunction than those without NP. To enhance the QoL and functional status of patients with CLBP, this study highlights the importance of appropriately diagnosing and treating NP.</p></sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".