Return-to-sports criteria used by professional team physicians in elite athletes after hip arthroscopy – a qualitative study
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to explore currently utilized readiness to Return to Sport (RTS) criteria after Hip Arthroscopy (HA) used in elite athletes to gain novel insights into the RTS decision-making process of professional team physicians. The authors hypothesized that even among this group of highly specialized physicians, there exists variability of measures and criteria used to determine RTS after HA. METHODS: A total of 15 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 HA. Themes and sub-themes were identified using a general inductive analysis and a coding process. A hierarchical approach in coding helped to link themes. RESULTS: Four key themes and several subordinate themes were identified from the interviews that seem to influence the return to sports decision. The most important RTS criteria were muscle strength (especially symmetric hip strength and muscle bulk with low side-to-side variance compared to the contralateral side) followed by pain-free sport-specific activity (pain-free drill skills and play at a lower level), physical examination (with major emphasis on the absence of hip pain with a painless hip range of motion compared to the contralateral side), and functional testing (including full squats, Ober test, FABER test, and pain-free FADIR position). CONCLUSION: Besides objective findings, including muscle strength, we identified time after surgery as well as subjective findings, including absence of pain and feedback of clinical team members that influence RTS decision after HA. We showed that even among specialized professional team physicians, the main criteria to RTS in these categories were not consistent necessitating the further 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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| 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".