Apprehension-Based Training: A Novel Treatment Concept for Anterior Shoulder Dislocation – A Case Report
Bibliographic record
Abstract
Background and Purpose: Conservative management of anterior shoulder dislocation (ASD) is associated with greater recurrence compared with surgical management. Current rehabilitation protocols may not adequately challenge shoulder stability to encourage adaptive coping strategies. Apprehension-based training (ABT) is a new treatment concept derived from the supine moving apprehension test (SMAT), a previously validated performance measure among patients with ASD. The purpose of this case report is to describe the application of ABT in a patient with recurrent ASD. Study Design: Case report. Case Description: The subject was a 23-year-old male with bilateral recurrent ASD. The subject underwent a 17-week exercise program involving gradual exposure to increased anterior instability loads based on the SMAT movement pattern. The Western Ontario Shoulder Instability Index (WOSI), Patient-Specific Functional Scale (PFPS), Tampa Scale of Kinesiophobia, SMAT, shoulder internal and external rotation muscle strength were measured via hand-held dynomometry before and after training. Outcomes: Following treatment, clinically meaningful gains in quality of life (WOSI) and shoulder function (PSFS) were noted. Kinesiophobia decreased, SMAT and shoulder internal rotator strength increased beyond their respective minimal detectable change. Four months after treatment, quality of life and shoulder function remained improved, and the subject reported a reduced rate of ASD. Discussion: Apprehension-based training involving gradual exposure to shoulder instability loads may hold potential for improving the management of patients with ASD. Further testing of this concept is warranted. Level of Evidence: 4, single case report.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".