Reliability of arthrometric measurement of shoulder lateral rotation movement in healthy subjects
Bibliographic record
Abstract
Numerous pathologic conditions of the shoulder result in loss of range of motion with lateral rotation being one of the most affected movements. Therefore, it is essential to know the reliability of the methods to measure this motion from a treatment evaluation perspective. The purpose of this study was to examine the reliability of the shoulder lateral rotation movement measured by an arthrometric method. Fifteen healthy subjects participated in the study. The passive range of motion in lateral rotation of the shoulder was measured with an arthrometric method for three positions of shoulder abduction and with three loads. Two evaluators took each measurement on two separate occasions. The reliability was good at 60 degrees and 90 degrees abduction with dependability indexes (phi) ranging from 0.77 to 0.87 and SEMs lower than 5 degrees. The reliability at 25 degrees abduction was lower with dependability indexes (phi) ranging from 0.52 to 0.66 and SEMs of 7 degrees or 8 degrees. Reliability was highest with the heaviest load reaching 0.66, 0.84, and 0.87 for the 25 degrees, 60 degrees, 90 degrees abduction angles, respectively. In all conditions of measurement, differences between intratester and intertester reliability were minimal. Reliability of the shoulder lateral rotation measured by arthrometry was dependent on angle of abduction and load used to move the shoulder. Further studies are needed to confirm these findings in individuals with impaired shoulders.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".