Comparison of Some Postural and Functional Factors in the Elderly With and Without a History of Falling
Bibliographic record
Abstract
Introduction: Falling is one of the serious issues of old age that occurs due to many factors. This research aimed to compare static and dynamic balance, walking speed, spinal curvature, and central stability of elderly people with and without a history of falling.Methods: The current research was a causal-comparative research. 60 elderly women aged between 60 and 70 from Tabriz voluntarily participated in the research. The participants were assigned into two groups with and without a history of falling. The fall history of the participants was measured by the Fall Efficacy Scale. The static and dynamic balance were evaluated by Sharpened-Romberg and Timed Up and Go tests, respectively. Walking speed was measured with a 10-meter walk test, core stability was measured with McGill endurance tests, and spinal curvature was measured with a flexible ruler. Independent t-test was used to compare the mean variables in two groups.Results: The results showed a significant difference between the static and dynamic balance indices, lumbar spine curvature, walking speed, and core stability in the elderly with and without a history of falling. However, there was no significant difference between the curvature index of the lumbar spine in the elderly with and without a history of falling.Conclusion: According to the research results, it seems necessary to develop and improve the indices of balance, walking speed, and core stability in people with a history of falling.
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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 | 0.002 |
| 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) | 0.002 | 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".