Safety and Efficacy of Pregabalin Treatment and Quality of Life in Patients Treated with Pregabalin in Painful Diabetic Peripheral Neuropathy
Bibliographic record
Abstract
The treatment modalities for painful diabetic peripheral neuropathy (PDPN) include antidepressants, anticonvulsants, capsaicin, membrane stabilizers and analgesics. Recent guidelines recommend pregabalin as a first line treatment for PDPN. But controversy exists about its efficacy and safety. The main objective of the study is to determine the safety, efficacy of pregabalin treatment for PDPN. An interventional cohort study was carried out in a tertiary care teaching hospital. Subjects who satisfy the inclusion criteria were included in the study after obtaining written informed consent. A total of 52 subjects were enrolled in the study. PDPN was confirmed by means of (1) Diabetic neuropathy symptom (DNS) score of more than one point, (2) Diabetic neuropathy examination score (DNE) of more than three points, (3) Neuropathic disability score (NDS) of more than 6 points and (4) Pain of more than 50% assessed by Visual Analog Scale (VAS). The subjects received pregabalin 75mg once daily for one week followed by 75 mg twice daily for further 3 weeks based on patient’s tolerability. Pain intensity was measured by using Short-Form McGill pain questionnaire. Safety of therapy was assessed from the incidence of adverse events including physical as well as laboratory evaluations. All study subjects’s enrolled received pregabalin. Study showed that pregabalin treatment significantly reduced pain in diabetic peripheral neuropathy with mean pain score 11.52 ± 9.30 (P value 0.00). The most common adverse effects of pregabalin were dizziness, peripheral edema, and somnolence. The study concluded that pregabalin significantly reduces pain and improve quality of life in PDPN.There was a dose-related increase in efficacy of pregabalin treatment. Moreover, there was a dose-related increase in incidence of most adverse events which are generally mild to moderate. Disclosure C. Radhakrishnan: None. A. Ut: None. S. K: None. N. Manikath: None. R.R. Cr: None.
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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) | 0.001 | 0.001 |
| 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) | 0.001 | 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".