Biomechanical, psychometric, and clinical evaluation of patients with orthopaedic shoulder instability: a cross-sectional study.
Bibliographic record
Abstract
Purpose The main aim of this study was to develop a biomechanical, subjective, and clinical characterisation of shoulder instability patients.Methods 38 adults with glenohumeral instability were enrolled. To assess the outcome variables, a goniometer, dynamometer, ultrasound, MoveUS Test, T-Fast Test, Western Ontario Shoulder Instability Index (WOSI), Quick-Piper fatigue scale, and Questionnaire on the athlete’s self-perception of returning to standardised training after an injury (RTP) were used.Results Results showed that WOSI was correlated with the range of motion (r = −0.379 to −0.513; p < 0.002), and with the Quick-Piper Fatigue Scale (r = 0.807; p < 0.001). Furthermore, RTP showed correlations with the range of motion (r = 0.357 to 0.370; p < 0.028), 120” T-Fast Test (r = 0.324; p = 0.047), WOSI (r = −0.746; p < 0.001), and Quick Piper Fatigue Scale (r= −0.389; p < 0.001). Likewise, muscle thickness at contraction showed a direct correlation with force peak (r = 0.334; p = 0.040); and T-Fast Test with pressure in push-ups (r = 0.441; p = 0.006), and explosive force peak (r = 0.355; p = 0.029). Moreover, three significant estimation models demonstrated that the quality of life, the capacity to return to play, and the endurance could be estimated using simple strength and mobility tests.Conclusions Goniometric, ergometric, dynamometric, sonographic, and subjective properties are greatly correlated in patients with glenohumeral instability, facilitating the development of short evaluations to predict the most complex variables.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".