A qualitative investigation to identify return to sports criteria after shoulder stabilization surgery used by professional team physicians
Bibliographic record
Abstract
Purpose: Purpose of this study is to explore currently utilized readiness to return to sports (RTS) criteria after shoulder stabilization surgery used in elite athletes to gain novel insights into the RTS decision making process of professional team physicians. Methods: 19 qualitative semi-structured interviews with professional team physicians were conducted by a single trained interviewer. The interviews were used to identify team physician concepts and themes regarding the criteria used to determine RTS after shoulder stabilization surgery. General inductive analysis and a coding process were used to identify themes and sub-themes arising from the data. A hierarchical approach in coding helped to link themes. Results: We were able to identify five key themes that participating physicians focused on to determine RTS decision making: external influence, objective and subjective criteria, time elapsed since surgery and type of sport. The most important RTS criteria included: range of motion and muscle strength followed by clinical joint stability, time since surgery, ability of sporting movement, psychological readiness, functional testing, absence of pain and allied team support. Conclusion: This study identified several main themes and subordinate minor themes as having the most influence on RTS decision after shoulder surgery. We showed that even among specialized professional team physicians, the main criteria to RTS in these categories were inconsistent necessitating the future development of specific RTS guidelines.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".