Computerized Functional Reach Test to Measure Balance Stability in Elderly Patients With Neurological Disorders
Bibliographic record
Abstract
BACKGROUND: The ability to maintain static and dynamic balance is a prerequisite for safe walking and for obtaining functional mobility. For this reason, a reliable and valid means of screening for risk of falls is needed. The functional reach test (FRT) is used in many countries, yet it does not provide some kinematic parameters such as shoulder or pelvic girdles translation. The purpose was to analyze video records measuring of distance, velocity, time length, arm direction and girdles translation while doing FRT. METHODS: A cross-sectional, descriptive study was conducted where the above variables were correlated to the mini-mental state examination (MMSE) for mental status and the Tinetti balance assessment test, which have been validated, in order to computerize the FRT (cFRT) for elderly patients with neurological disorders. Eighty patients were tested and 54 were eligible to serve as experimental group. The patients underwent the MMSE, the Tinetti test and the FRT. LAB view software was used to record the FRT performances and to process the videos. The control group consisted of 51 healthy subjects who had been previously tested. RESULTS: The experimental group was not able to perform the tests as well as the healthy control subjects. The video camera provided valuable kinematic results such as bending down while performing the forward reach test. CONCLUSIONS: Instead of manual measurement, we proposed to use a cheap with fair resolution web camera to accurately estimate the FRT. The kinematic parameters were correlated with Tinetti and MMSE scores. The performance values established in this study indicate that the cFRT is a reliable and valid assessment, which provides more accurate data than "manual" test about functional reach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".