Three-dimensional measurements of scapular kinematics: Interrater reliability and validation of a skin marker-based model against an intracortical pin model
Bibliographic record
Abstract
A skin marker-based motion capture model providing measures of scapular rotations was recently developed. The aim of this study was to investigate the concurrent validity and the interrater reliability of the model. Shoulder range of motion (RoM) and activities of daily living (ADL) were tested in healthy volunteers with reflective markers on the scapula and thorax. To investigate the validity, the model was compared to simultaneous data collection from markers on a scapular intracortical pin. The interrater reliability was tested by comparing the skin marker-based protocol performed by two investigators. The mean root mean square error (RMSE) and the intraclass correlation coefficient (ICC(2,1)) were calculated to determine the validity and the interrater reliability, respectively. Eight subjects were included in the validity test: female/male = 2/6, mean (SD) age 35.0 (3.0) and BMI 23.4 (3.3). The mean RMSE of all scapular rotations ranged 2.3-6.7° during shoulder RoM and 2.4-7.6° during ADL. The highest errors were seen during sagittal and scapular plane flexions, hair combing and eating. The reliability test included twenty subjects: female/male = 8/12, mean (SD) age 31.4 (4.9) and BMI 22.9 (1.7). The ICC(2,1) for measuring protraction ranged 0.07-0.60 during RoM and 0.27-0.69 for ADL, for upward rotation the corresponding ICC(2,1) ranged 0.01-0.64 and 0.38-0.60, and anterior tilt 0.25-0.83 and 0.25-0.62. The validity and interrater reliability of the model are task dependent, and interpretation should be made with caution. The model provides quantitative measurements for objective assessment of scapular movements and can potentially supplement the clinical examination in certain motion tasks.
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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 | 0.012 | 0.028 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".