The Prevalence and Impact of Chronic Pain with Neuropathic Pain Symptoms in the General Population
Bibliographic record
Abstract
OBJECTIVE: We performed a prevalence estimate of chronic pain with neuropathic pain (NeP) symptoms to determine its frequency and associations with morbidity. DESIGN: We conducted a telephone-based survey based upon a random sampling of both urban and rural households of the general population in one Canadian province to determine NeP prevalence and its impact upon financial well-being and quality of life. OUTCOME MEASURES: Telephonic use of the DN4 questionnaire (DN4Q), used to identify NeP symptoms in those patients with chronic pain, was validated within selected clinical populations of chronic pain. Epidemiological data was obtained for all subjects. EuroQoL (EQ)-5D data estimating quality of life was measured. RESULTS: Chronic pain was present in 35.0% of the surveyed population of 1,207 subjects, with NeP symptoms present in 17.9%. The NeP group had significantly more pain, was female predominant, had a greater belief of being economically disadvantaged, suffered from more restrictions in mobility and in usual activities, and had overall lower EQ-5D utility scores compared with subjects with non-NeP. DN4Q validation demonstrated that pain entities not normally defined as NeP are recorded as such using the DN4Q, and that a spectrum of NeP features may occur across a host of painful conditions. CONCLUSION: Despite limitations of the DN4Q, symptoms of NeP may be more prevalent in the general population than expected and has a greater impact upon patients' lives than non-NeP. Limitations of the DN4Q may relate to the concept of a spectrum of NeP existent amongst heterogenous NeP and non-NeP syndromes.
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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".