Impact of Type‐2 Diabetes Time Since Diagnosis on Elderly Women Gait and Functional Status
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The gait, mobility and lower-limb strength alterations of diabetic elderly women without symptoms of diabetic neuropathy in different periods of the chronic disease can contribute to an early functional diagnosis, allowing prevention of adverse outcomes like falls and disability. This could also contribute to the development of interventions, cures and physiotherapy practice for this population. The aim of this study was to verify the impact of type-2 diabetes mellitus time since diagnosis on gait and functional status of elderly women. METHODS: Eighty-two diabetic elderly women without neuropathic symptoms participated and divided in two groups: 1) 49 elderly (71.4 ± 4.8 years) with less than 10 years of type-2 diabetes mellitus diagnosis, and 2) 33 elderly (70 ± 4.5 years) with 10 or more years of type-2 diabetes mellitus diagnosis. Outcomes were spatiotemporal gait parameters (speed, cadence, step length, base of support, stance time, swing time, and double support time) assessed through GAITRite® system, and functional status assessed using the Timed Up and Go test and five times sit-to-stand test. To compare spatiotemporal gait variables and performance on functional tests between groups, multivariate analysis of variance and Mann-Whitney test were performed, respectively. RESULTS: The group with 10 or more years of diagnosis showed lower gait speed and smaller step length (112.3 cm/s; 59.2 cm) compared with the group with less than 10 years of diagnosis (122.9 cm/s; 62.4 cm). In relation to Timed Up and Go test and five times sit-to-stand test, there were no statistically significant differences between the groups. CONCLUSION: Type-2 diabetes mellitus time since diagnosis has a negative impact on gait speed and step length, but not on functional status of the elderly women. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".