Inter-session Reliability of Glenohumeral Internal and External Rotation Range-of-motion Measurements is Unaffected by Use of Applied Load Feedback
Bibliographic record
Abstract
This study evaluated whether real-time applied load feedback and a predefined applied load limit improved inter-session reliability and measurement error of passive glenohumeral rotation range-of-motion measurements. Twenty-one male recreational overhead athletes completed two data collection sessions, approximately 1-week apart. Measurements of internal and external rotation range-of-motion and clinician-applied loads were obtained and side-to-side differences were calculated. A load limit of 4 Nm was used for feedback trials. Perception of a capsular end-feel was used for no-feedback trials. Intraclass correlation coefficients (ICCs), standard errors of measurement (SEMs) and minimal detectable changes (MDCs) were derived. ICCs from single trials with and without feedback ranged from 0.72–0.93 and 0.63–0.89, respectively. SEMs ranged from 2.6°-6.1° with feedback and 3.3°-6.0° without feedback. MDCs from single trials with and without feedback ranged from 7.2°-16.8° and 9.1°-16.7°, respectively. Applied load feedback did not improve, already moderate to excellent, inter-session reliability and SEM for passive glenohumeral rotation range-of-motion measurements.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".