Intrarater Reliability and Analysis of Learning Effects in the Y Balance Test
Bibliographic record
Abstract
While the general reliability of the Y balance test has been previously found to be excellent, earlier reviews highlighted a need for a more consistent methodology between studies. The purpose of this test-retest intrarater reliability study was to assess the intrarater reliability of the YBT using different methodologies regarding normalisation for leg length, number of repetitions, and score calculation. Sixteen healthy adult novice recreational runners aged 18-55 years, both women and men, were reviewed in a laboratory environment. Mean calculated scores, intraclass correlation coefficient, standard error of measurement, and minimal detectable change were calculated and analysed between different leg length normalisation and score calculation methods. The number of repetitions needed to reach a plateauing of results was analysed from the mean proportion of maximal reach per successful repetition. The intrarater reliability of the YBT was found to be good to excellent, and it was not affected by the method of score calculation or leg length measurement. The test results plateaued after the sixth successful repetition. Based on this study, it is suggested to use anterior superior iliac spine-medial malleolus length for leg length normalisation because this method was proposed in the original YBT protocol. At least seven successful repetitions should be performed to reach a result plateau. The average of the best three repetitions should be used to mitigate possible outliers and account for the learning effects seen in this study.
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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 | 0.050 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".