Bibliographic record
Abstract
BACKGROUND: The legitimacy of manual muscle testing (MMT) is dependent in part on the reliability of assessments obtained using the procedure. OBJECTIVE: The purpose of this review, therefore, was to consolidate findings regarding the test-retest and inter-rater reliability of MMT from studies meeting inclusion and exclusion criteria. METHODS: An electronic search of PubMed, Scopus, and CINAHL databases and a hand search were conducted to identify articles addressing the test-retest or inter-rater reliability of MMT. Data on participants, testing specifics, and findings regarding reliability were extracted. RESULTS: Of 189 unique articles identified, 9 were found to meet inclusion/exclusion criteria. The studies were highly variable in regard to the population tested, MMT procedure and scoring, and findings. Nevertheless, based on pairwise comparisons, substantial or almost perfect test-retest and inter-rater agreement was demonstrated for most muscle actions tested. CONCLUSIONS: Reliable assessments of strength may be obtained by MMT but not assumed. Further research is required to address the reliability of MMT across pathologies, muscle groups, and test procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".