Measuring shoulder proprioception: Introducing a new clinical tool - The Shoulder Proprioception Reaching Test (SPReT)
Bibliographic record
Abstract
Abstract Background: Proprioception, our sense of joint awareness, is a sub-category of somatosensation, known as our “sixth sense”. It is part of our neuromuscular system, responsible for maintaining joint stability and preventing injury, particularly amongst shoulders. Proprioception is affected by injury and a decreased sense of proprioception can cause a predisposition to injury. Despite the importance of proprioception for shoulder stability, there remains no confident way to measure shoulder proprioception in a clinic. Purpose: To present a new clinical tool – The Shoulder Proprioception Reaching Test (SPReT) and to establish the discriminant validity, intra-rater and absolute reliability properties. Study Design: Cross-sectional clinical measurement study. Methods: A convenience sample of forty healthy participants was used. A single session (120 minutes) involved two bilateral evaluations of reaching movements using the SPReT, with a 30-minute rest period. Twenty participants were randomly selected to further perform a shoulder fatigue protocol of the dominant shoulder, immediately followed by a third SPReT evaluation. The SPReT involves active reaching movements in standing and with eyes closed, towards 7 targets in a star formation (active joint position sense). Discriminant validity (paired t tests), intra-rater reliability (intraclass correlation coefficients [ICC]) and absolute reliability (standard error of measurement [SEM] and minimal detectable change [MDC]) were determined. Results: The SPReT supports moderate (ICC = 0.45 – 0.71) to good (ICC = 0.77) intra-rater reliability with SEMs ranging from 1.17 to 2.63 cm and MDC90 from 2.72 to 6.12 cm for all targets. A fatigue effect was found with the superior and the superior-lateral right targets (P < .05), suggesting possible discriminant validity with the SPReT tool during elevated reaching movements.Conclusions: The superior target demonstrated the highest discriminant validity, intra-rater and absolute reliability properties and takes less than 5 minutes to evaluate. The evaluation of the superior target only may be suitable for clinical practice upon further methodological investigation for validity, reliability and responsiveness. Future research in encouraged and continues to be ongoing amongst a pathological population. Level of evidence: Level III cross-sectional study.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".