The Association of Diabetes with Knee Pain Severity and Patterns in People with Knee Osteoarthritis
Bibliographic record
Abstract
Background: As one of the most common forms of joint disease, osteoarthritis (OA) causes progressive impairment in adults because of the deterioration of articular cartilage in the joints. Multiple variables, including comorbidities (such as diabetes mellitus, hypertension, obesity, and dyslipidemia), lifestyle choices, food, age, and genetics, might influence the occurrence, development, and severity of OA symptoms. Objective: To assess knee pain severity & patterns in cases with OA & OA with diabetes mellitus. Patients and Methods: This is cross-sectional research that was done in Benha University Hospitals on 100 cases, which were separated into two groups: Group one included fifty OA patients and group two consisted of fifty OA patients with diabetes mellitus (DM). Group two was divided into two subgroups: good control and poor control patients according to diabetes control. Knee pain was measured according to three subscales: WOMAC (The Western Ontario and McMaster Universities OA Index), KOOS (Knee Injury and OA Outcome Score) and VAS (visual analogue scale). Results: There was a statistically significant variance amongst the examined groups concerning diabetes parameters (HbA1c, fasting glucose and two hours post prandial), Kellgren-Lawrence scale, correlation of diabetes duration with other parameters of pain in OA patients and correlation of HbA1c with other parameters of pain among OA patients with DM. Conclusion: Our study concluded that WOMAC pain subscale was significantly increased with worsening of DM and KOOS pain subscale was significantly decreased with worsening of DM. This suggests the crucial need for management of DM to achieve better outcomes of OA.
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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".