FACTORS INFLUENCING THE SEVERITY OF PAIN IN PATIENTS WITH PERIPHERAL DIABETIC NEUROPATHY
Bibliographic record
Abstract
Objective: The principal aim of this study was to identify factors influencing the severity of peripheral diabetic neuropathy pain (PDNP), a symptom of the common neurological complication of diabetes mellitus, and peripheral diabetic neuropathy.Methods: A cross-sectional study was performed using two self-administered questionnaires among subjects recruited from outpatient clinics at Hospital Tengku Ampuan Afzan, Kuantan, Malaysia. The Neuropathic Pain-4 tool was used to evaluate the presence of PDNP, and the Short-Form McGill Pain Questionnaire (MPQ) was used to characterize and determine the severity of PDNP. Sociodemographic and clinical data were collected from the patients.Results: The MPQ indicated that most patients reported experiencing mild pain for all sensory pain descriptors other than throbbing and aching (mostly reported to be moderate) and hot-burning (mostly reported to be no pain). The severity of pain was found to be significantly related to the length of time for which the patients had suffered from diabetes in those patients who had been diagnosed over 10 years previously (p=0.04). Indian patients reported a higher severity of pain overall (p=0.04). No significant relationship was found between pain severity and any of the following factors: Type of diabetes (I or II), gender, smoking status, alcohol consumption, obesity, medication taken, or presence of other diseases.Conclusion: In this study, most patients with PDNP reported the severity of the pain to be “mild.” The pain severity may be influenced by a patient’s ethnicity and the length of time for which they have suffered from diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".