A Cross-Sectional Study on Clinical Profiles and Quality of Life of Patients with Neuropathic Pain
Bibliographic record
Abstract
Background: Due to nerve damage in the central and peripheral nervous systems, people with neuropathic pain experience acute pain. This study evaluated the health effects of neuropathic pain and different types of pain according to various neuropathies. Objective: To evaluate the clinical characteristics and QOL of neuropathic pain patients. Study the most frequently given medications, evaluate patients with various types of neuropathic pain, determine the characteristics of neuropathic pain, analyse the effects of neuropathic pain on general health. Materials and Methods: A total of 102 patients were enrolled according to the inclusion and exclusion criteria. Patients were interviewed using Short-Form McGill Pain Questionnaire-2, RAND 36 Item Health Survey 1.0 Questionnaire. Percentage, mean, chi square and standard deviation was used mainly in Statistical analysis. Result: Out of 102 patients, 44.12% were female, and 55.88% were male. Mean age of male and female was 46.60 and 47.42 years respectively. Radiculopathy patients were in the majority (43.1%), followed by peripheral neuropathy (37.3%), myelopathy (16.7%), sciatic neuropathy (2%), and brachial plexopathy (1%). Diabetes was highest to cause peripheral neuropathy. The most commonly prescribed drug was pregabalin, and combination were gabapentin and nortriptyline. The total means of all subscales of SF-MPQ-2 questionnaire was 4.56(2.10). Mean of physical component and emotional component summary was 34.30(17.54), and 42.38(15.74). Conclusion: Diabetes, trauma, weightlifting can also cause neuropathic pain. Diabetes was among top cause for peripheral neuropathy. Burning, itching, numbness, tingling, cold freezing pain were prevalent. The emotional function of patients was better than physical function.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".