Adjacent Joint Restriction Differentially Influences Intra- and Inter-rater Reliability and Agreement of Goniometric Measurements
Bibliographic record
Abstract
BACKGROUND: Reliability and agreement of goniometric measurements can be altered by variations in measurement technique such as restricting adjacent joints to influence bi-articular muscles. It is unknown if the influence of adjacent joint restriction is consistent across different range of motion (ROM) tests, as this has yet to be assessed within a single study. Additionally, between-study comparisons are challenged by differences between methodology, participants and raters, obscuring the development of a conceptual understanding of the extent to which adjacent joint restriction can influence goniometric ROM measurements. PURPOSE: To quantify intra- and inter-rater reliability and levels of agreement of goniometric measurements across five ROM tests, with and without adjacent joint restriction. STUDY DESIGN: Descriptive reliability study. METHODS: Three trained and experienced raters made two measurements of bilateral ankle dorsiflexion, first metatarsophalangeal dorsiflexion, hip extension, hip flexion, and shoulder flexion, with and without adjacent joint restriction. Intraclass correlation coefficient (ICC), standard error of measurement (SEM), along with participant, measurement/rater and random error variance were estimated. RESULTS: Eleven females (age 21.4 ±2.3 years) and 19 males (age 22.1 ±2.8 years) participated. Adjacent joint restriction did not influence the reliability and agreement in a consistent way across the five ROM tests. Changes in the inter-rater reliability and agreement were more pronounced compared to the intra-rater reliability and agreement. Assessing variance components (participant, measurement/rater and random error variance) that are used to calculate the ICC and SEM, improved interpretation of ICC and SEM scores. CONCLUSION: The effects of adjacent joint restriction on reliability and agreement of goniometric measurements depend on the ROM test and should be considered when comparing measurements between multiple raters. Reporting variance components that are used to calculate the ICC and SEM can improve interpretation and may improve between-study comparisons, towards developing a conceptual framework to guide goniometric measurement technique. LEVEL OF EVIDENCE: 3b.
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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 | 0.040 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".