Technology-based balance performance assessment can eliminate floor and ceiling effects
Bibliographic record
Abstract
Many clinical measurement tools for balance have ceiling effects. Technology-based assessments using virtual reality systems such as the Computer-Assisted Rehabilitation Environment (CAREN) may provide a way to develop objective, quantitative measures that scale from low to high levels of difficulty. Our objective was to: (1) develop a performance assessment tool (PAT) for the CAREN; (2) quantify the reliability of the tool; (3) validate the scores against clinical balance measures; and (4) compare the scores from a population with balance impairments to those from able-bodied individuals in a cross-sectional validation study. Three games were developed on the CAREN and tested on 49 participants (36 able-bodied and 13 with impaired mobility). For each module, the corresponding measures were transformed into scores using a series of functions such that ceiling and flooring effects would be minimized. The results showed an association between scores and age, an overlap in scores from impaired high-performance individuals and able-bodied low performance individuals, and a correlation of PAT scores with other clinical tests. Several of the limitations of current clinical tools, including floor and ceiling effects, were overcome by the PAT, suggesting that the PAT can be used to monitor the effect of rehabilitation and training.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".