Normative Data for the BTrackS Balance Test of Postural Sway: Results from 16,357 Community-Dwelling Individuals Who Were 5 to 100 Years Old
Bibliographic record
Abstract
Background: Postural sway is routinely assessed because increased postural sway is associated with poorer performance of activities of daily living, higher rates of residential care, and increased risk of falling. Force plate technology is one of the most sensitive and objective means of assessing postural sway in the clinic. Objective: The aim of this study was to provide the first set of normative data for the BTrackS Balance Test (BBT) of postural sway. Design: The design was descriptive and population based. Methods: BBT results from 16,357 community-dwelling individuals who were 5 to 100 years old were accumulated and assessed for effects of age, sex, height, and weight. Percentile rankings were calculated for significant groupings. Results: BBT results were dependent on age and sex but not height or weight. Therefore, percentile rankings were determined for male and female individuals in each age category, with no consideration of participant height or weight. Limitations: Data were collected by third-party practitioners with various backgrounds in more than 50 locations across the United States and Canada. There was an imbalance in the sample sizes for age and sex groupings. Conclusions: The findings of this study represent the largest normative dataset ever published for postural sway results. Normative data on the BBT can assist in determining abnormalities in postural sway, which have been linked to negative clinical outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".