Examination of the Construct Validity of Nextiles Sleeve as a Measure of Peak Elbow Varus Torque
Bibliographic record
Abstract
Context New wearable circuited fabric biomechanic technology that can be used in a more ecological context to measure elbow-varus torque and throw count is now available but needs to be validated compared with markerless biomechanics. Objective To examine the construct validity of the Nextiles Arm Sleeve by assessing its agreement and convergence with markerless 3-dimensional (3D) biomechanics used as a reference for (1) peak elbow-varus torque and (2) pitches thrown with separate analyses for fastballs, breaking balls, and changeups. Design Cross-sectional study. Setting Laboratory. Patients or Other Participants A total of 29 collegiate pitchers (age = 19.5 ± 1.3 years, height = 1.88 ± 0.06 m, mass = 91.7 ± 9.5 kg, body mass index = 26.2 ± 1.9 kg/m 2 ) participated. Intervention(s) Pitchers were assessed simultaneously via the Nextiles sleeve and markerless 3D motion capture (KinaTrax) as they threw fastballs, breaking balls, and changeups to a catcher at regulation distance. Main Outcome Measure(s) Nested Bland-Altman limits of agreement (LoA) for construct validity, mixed-effects linear regressions for convergence validity, and intraclass correlation coefficients (ICCs) for test-retest reliability were calculated between the Nextiles sleeve and 3D biomechanics for elbow-varus torque. We determined that an a priori acceptable threshold for LoA and convergence was a 95% CI width of 0.15 and 95% CI lower limit of 0.50. Results Pitchers threw 200 pitches (fastball: 72%, n = 143; breaking ball: 14%, n = 27; changeup: 15%, n = 30), with 182 pitches recorded by the Nextiles sleeve and 18 pitches (n = 3 pitchers) not recorded due to technical errors. Agreement and validity for pitch measures (mean difference [lower, upper] LoA = 6.7 N·m [–26.2, 39.6 N·m]; β = 0.31 [0.12, 0.52]; Nextiles sleeve ICC [95% CI] = 0.96 [0.93, 0.98]; KinaTrax ICC [95% CI] = 0.97 [0.94, 0.98]) were higher compared with agreement for body weight × height (BW×H; mean difference [lower, upper] LoA = 0.37 BW×H [–1.41, 2.45 BW×H]; β = 0.37 [–1.41, 2.15]; Nextiles sleeve ICC [95% CI] = 0.93 [0.88, 0.96]; KinaTrax ICC [95% CI] = 0.95 [0.92, 0.98]). We found no interaction per pitch type ( P = .47) or pitch thrown ( P = .58). Conclusions The Nextiles sleeve collected pitch counts accurately except for 3 pitchers for whom it did not collect any pitches thrown due to technical errors. However, the demonstrated LoA and convergence were beyond the a priori acceptability threshold and lower than the a priori defined acceptable lower bound of the 95% CI. These results suggest that the Nextiles sleeve does not measure the same construct as KinaTrax.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".