Repeatability of two methods for estimating scapular kinematics during dynamic functional tasks
Bibliographic record
Abstract
Best practices for scapular motion tracking are still being determined. The repeatability of different scapular kinematic procedures needs to be evaluated. The purpose of this study was to assess the test-retest reliability of two scapular kinematic procedures: double calibration with AMC (D-AMC) and individualized linear modelling (LM). Ten healthy participants had their upper body movement tracked with optical motion capture in two identical sessions. Five scapular calibration poses were performed, and seven dynamic functional tasks were tested. Scapular angles were calculated from both procedures (D-AMC vs LM). The D-AMC approach uses two poses (neutral and maximum elevation) and tracks the scapula with a rigid cluster, while the LM approach predicts scapular positioning from humeral angles based on equations built from the calibration pose data. Angle waveforms and repeatability outcomes were compared. Internal and upward rotation angle waveforms were significantly different (p < 0.05) between kinematic procedures for some tasks, with maximum mean differences up to 17.3° and 23.2°, respectively. Overall, repeatability outcomes were similar between procedures, but the LM approach was slightly better for tilt and the D-AMC approach was notably improved for upward rotation in certain tasks. For example, minimal detectable changes during the Forward Transfer ranged from 6.9° to 11.9° for the D-AMC and 8.9° to 25.3° for the LM. Discrepancies between procedures may be a function of the calibration poses chosen. Additional calibration poses may improve the comparisons between procedures.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".