Return to sport decisions after an acute lateral ankle sprain injury: introducing the PAASS framework—an international multidisciplinary consensus
Bibliographic record
Abstract
Background Despite being the most commonly incurred sports injury with a high recurrence rate, there are no guidelines to inform return to sport (RTS) decisions following acute lateral ankle sprain injuries. We aimed to develop a list of assessment items to address this gap. Methods We used a three-round Delphi survey approach to develop consensus of opinion among 155 globally diverse health professionals working in elite field or court sports. This involved surveys that were structured in question format with both closed-response and open-response options. We asked panellists to indicate their agreement about whether or not assessment items should support the RTS decision after an acute lateral ankle sprain injury. The second and third round surveys included quantitative and qualitative feedback from the previous round. We defined a priori consensus being reached at >70% agree or disagree responses. Results Sixteen assessment items reached consensus to be included in the RTS decision after an acute lateral ankle sprain injury. They were mapped to five domains with 98% panellist agreement—PAASS:Pain (during sport participation and over the last 24 hours),Ankle impairments (range of motion; muscle strength, endurance and power),Athlete perception (perceived ankle confidence/reassurance and stability; psychological readiness),Sensorimotor control (proprioception; dynamic postural control/balance),Sport/functional performance (hopping, jumping and agility; sport-specific drills; ability to complete a full training session). Conclusion Expert opinion indicated that pain severity, ankle impairments, sensorimotor control, athlete perception/readiness and sport/functional performance should be assessed to inform the RTS decision following an acute lateral ankle sprain injury. Trial registration number ACTRN12619000522112.
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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 | 0.226 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.006 | 0.008 |
| 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".