Validation of a commercially available inertial measurement unit for recording jump load in youth basketball players
Bibliographic record
Abstract
A high incidence of overuse knee injuries among youth basketball players may be attributed to number of jumps. Wearable technology may be an effective tool for measuring jump load compared to traditional counting methods. The purpose of this study was to validate a commercially available jump counter (VERT® Classic) in youth basketball practices and games, and to identify the characteristics (i.e., height, direction, takeoff) of jumps recorded by the VERT® Classic. 46 (19F, 27M) youth basketball players wore a VERT® Classic and were recorded on video during games and practices. The number of jumps recorded by the VERT® Classic and evaluated by video raters were compared for each jump characteristic using intraclass correlation coefficient (ICC(3,k)), mean offset, and limits of agreement. The number and percent of VERT® Classic jumps and corresponding video jumps according to timestamp were reported. VERT® Classic jumps had excellent reliability with video-counted jumps over 15 cm (ICC(3,k) = 0.958), with a mean offset of −2.4 jumps (fewer VERT® Classic) and limits of agreement −12.6 to 7.8 jumps. Pairs of corresponding jumps represented 68.0% of total video jumps and 92.0% of VERT® Classic jumps. The VERT® Classic can provide an estimate of jump load in youth basketball.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".