Posterior Shoulder Instability Classification, Assessment, and Management: An International Delphi Study
Bibliographic record
Abstract
Objective To reach consensus among international shoulder experts on the most appropriate assessment and management strategies for posterior shoulder instability (PSI). Design Delphi. Methods In phase 1 of the study, we reviewed the literature, generated the Delphi items, created the survey, and identified clinical experts. In phase 2 of the study, clinical shoulder experts (physical therapists, orthopaedic surgeons, sports medicine physicians, and researchers) participated in a 3-round e-Delphi survey. For consensus, we required a minimum of 70% agreement per round. Descriptive statistics were used to present the characteristics of the respondents, the response rate of the experts in each round, and the consensus for PSI classification, assessment, and management. Results Round 3 was completed by 47 individuals from 5 different countries. The response rate ranged from 57/70 (81%) to 47/50 (94%) per round. Respondents agreed on 3 subgroups to define PSI: traumatic (100% agreement), microtraumatic (98% agreement), and atraumatic (98% agreement). Conclusion International shoulder experts agreed that the clinical presentation, management strategy, and outcome expectations differ for traumatic, microtraumatic, and atraumatic PSI. Their recommendations provide a framework for managing these subgroups, with additional consideration of sport and work participation and subsequent risks. J Orthop Sports Phys Ther 2020;50(7):373–380. Epub 29 Apr 2020. doi:10.2519/jospt.2020.9225
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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 | 0.098 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".