Pharmacological target therapy of neuropathic pain and patient-reported outcomes in patients with chronic low back pain in Korea
Bibliographic record
Abstract
A number of studies have demonstrated an association of neuropathic pain and chronic low back pain (CLBP), but the outcome difference in each medical management is poorly understood. This study is aimed to investigate treatment patterns of neuropathic pain in CLBP patients and to explore patient-reported outcomes (PROs) including quality of life (QoL) and functional disability by treatment patterns.Data were extracted from the neuropathic low back pain (NLBP) outcomes research. It was a multicenter and cross-sectional study in which 1200 patients were enrolled at 27 general hospitals, from 2014 to 2015. Of total, 478 patients classified as neuropathic pain were used for this subgroup analysis. The patients were divided into 2 groups according to treatment patterns (with vs. without the targeted therapy [TT] of neuropathic pain). Demographic and clinical features were collected by chart reviews and PROs were measured by patient's survey. QoL was assessed by EuroQoL 5-dimension (EQ-5D) questionnaire. Functional disability was measured by the Quebec Back Pain Disability Scale (QBPDS). Multiple linear regression analyses were conducted to compare the PROs between TT group and non-targeted therapy (nTT) group.Among the NLBP patients (mean age 63years, female 62%), EQ-5D index, EuroQoL-Visual Analog Scale (EQ-VAS), and QBPDS Scores (mean ± standard deviation) were 0.40 ± 0.28, 54.98 ± 19.98, and 46.03 ± 21.24, respectively. Only 142 (29.7%) patients had pharmacological TT of neuropathic pain. Univariate analyses revealed no significant mean differences between TT group and nTT group in the EQ-5D index (0.41 ± 0.27 and 0.39 ± 0.28), EQ-VAS (56.43 ± 18.17 and 54.37 ± 20.69), and QBPDS (45.31 ± 21.32 and 46.31 ± 21.24). After adjustment with covariates, TT group had higher scores of EQ-5D index (β = 0.07; P < 0.01) and EQ-VAS (β = 4.59; P < 0.05) than the nTT group. The TT group's QBPDS score was lower than the nTT group, although its statistical significance still has not been reached (β = -4.13; P = 0.07).We found that considerable proportion of the NLBP patients remains untreated or undertreated. Although TT group had significantly better QoL than nTT group, only 29.7% of NLBP patients had pharmacological TT. Therefore, clinicians should consider using TT for better QoL of neuropathic pain patients.
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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 | 0.002 |
| 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 | 0.000 |
| 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".