The Study of Relationship between Painful Diabetic Peripheral Neuropathy and Functional Status of Older People In Kuantan, Pahang.
Bibliographic record
Abstract
Objective: The purpose of this study was to determine the relationship between painful diabetic peripheral neuropathy (PDPN) and functional status among older people in Kuantan, Pahang. Method: A cross sectional design was employed in this study. A total of 300 respondents were selected by convenience sampling method from Endocrine Clinics HTAA and selected clinics in Kuantan. The Short Form McGill Pain Questionnaire 2 (SFMPQ2) was used to characterized and determine the severity of PDPN and Katz ADL Index was used to determine the activities of patient’s daily living. Data was analysed by using SPSS 20.0 version and Descriptive analysis such as mean, standard deviation (SD) and frequency were utilized to describe the characteristics of the respondents. The mean age of the respondents was 68 years old. Result: Spearmen Correlation was utilized to determine the relationship between painful diabetic peripheral neuropathy and functional status. Kruskal Wallis test and Mann Whitney test was conducted in finding the relationship between painful diabetic peripheral neuropathy and socio demographic and clinical characteristic. Conclusion: The result indicates that functional status not significantly associated with the pain. However, race, duration of diabetes mellitus and types of treatment were significantly contributed to pain.
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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 | 0.000 | 0.001 |
| 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".