Normative data and inter-examiner reliability of the upper quarter Y-balance test
Bibliographic record
Abstract
Background/Aims Physical performance measures, such as the lower quarter Y-balance test, can be used to determine performance ability or functional limitations. The aim of this study was to establish a normative data set for the upper quarter Y-balance test and evaluate inter-rater reliability. Methods Healthy participants, with no current upper extremity complaint or balance issues, were recruited. Participants undertook the upper quarter Y-balance test by reaching in three directions: medial; superolateral; and inferolateral. Each participant completed three reaches on each side. The mean of the second and third reach distances was used for the analysis and reach distance was normalised to limb length. Results Mean participant age was 24.3 (± 5.6) years. Statistically significant differences between males and females were observed for all reach directions (both raw and normalised data), with large to very large effect sizes (P<0.001, effect size (r)=0.82–1.92). Results showed statistically significant differences between left- and right-hand dominance with left inferolateral reach (P=0.038, z=−2.076, r=0.21). Inter-rater reliability was excellent, with an intraclass correlation coefficient (3,1) of 0.98. Conclusions The results suggest that males have a statistically significantly larger reach in all directions and that participants reach significantly further when testing the dominant arm versus the non-dominant arm. The data presented here provide researchers with a substantive normative data set that will allow for comparison with symptomatic populations.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".