Comparison of Differences in Handgrip Strength between Diabetic and Non-diabetic Patients in Central Kerala: An Analytical Study
Bibliographic record
Abstract
Abstract Introduction: Type 2 diabetes (T2D) mellitus has many well-known complications, including involvement of the eyes, kidneys, nerves, and blood vessels. These complications are a significant cause of morbidity and reduced quality of life in diabetics. Though sensory deficits are well-established, motor complications of T2D are less understood. This study aims to compare handgrip strength (HGS) among patients with and without diabetes. Methods: Fifty-four patients with diabetes who presented to the outpatient department (OPD) of a secondary care hospital in rural Kerala were recruited into the study. Controls were selected using the very next patient presenting to the OPD. After informed consent, a handheld Jamar dynamometer was used to measure HGS in both arms in both groups. The paired t test was used to compare the difference in HGS in both groups. Results: Both groups were comparable in age, gender, and body mass index. This study found significantly lower HGSs in diabetic participants compared to non-diabetic individuals ( P < 0.001). Negative correlations were observed between HGS and HbA1c levels for both genders (R = −0.33 females, R = −0.39 males). Using a paired t test, these differences in the right and left HGSs were significant ( P < 0.001). Conclusions: Even though the discussions on morbidity in diabetes have primarily been described in terms of sensory deficits, this paper provides data to demonstrate the effects of diabetes on motor systems as well. It still needs to be determined how much this difference translates into a functional loss. However, early involvement of physical medicine and rehabilitation teams may improve the quality of life in diabetics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".