Reliability, validity, and responsiveness of pinch strength assessment: a systematic review
Bibliographic record
Abstract
Purpose To summarize and critically appraise the quality of studies investigating psychometric properties of pinch strength assessment.Methods Medical literature up to February 2024 was searched for studies reporting on at least one measurement property of pinch strength assessment. The quality of the evidence and the risk of bias were rated using COSMIN 2018 guidelines.Results Thirty-three studies (1962 participants) were included. The majority (16/19) of reliability studies were of adequate to very good quality. Seven of 12 studies of validity were rated as adequate or very good. The quality of the eight responsiveness studies was adequate. Reliability was good to excellent (ICC > 0.75) for neurological conditions, and excellent (ICC > 0.90) for musculoskeletal disorders and healthy participants. Pinch strength showed strong to very strong correlations with grip strength (r = 0.72–0.92), moderate to strong correlations with assessments of dexterity (r = 0.78–0.80), and weak to moderate correlation with patient-reported outcome measures (r = 0.03–0.50). Varied results were found for pinch strength responsiveness in a small number of studies.Conclusions Pinch strength assessment is reliable. Validity and responsiveness are less reported, but there is a strong correlation between pinch and grip strength, and a moderate correlation with dexterity.
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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 | 0.021 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".