Return-to-Play Outcomes in Elite Athletes After Cervical Spine Surgery: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Return-to-play (RTP) outcomes in elite athletes after cervical spine surgery are currently unknown. OBJECTIVE: To systematically review RTP outcomes in elite athletes after anterior cervical discectomy and fusion (ACDF), cervical disc replacement (CDR), or posterior foraminotomy (PF) surgery. DATA SOURCES: . STUDY SELECTION: Eligible studies included those that reported RTP outcomes in elite athletes after cervical spine surgery. STUDY DESIGN: Systematic review. LEVEL OF EVIDENCE: Level 4. DATA EXTRACTION: Data were extracted by 2 independent reviewers. RESULTS: The primary outcomes of interest were rates and timing of RTP. Secondary outcomes included performance on RTP. A total of 1720 studies were initially screened. After inclusion criteria were applied, 13 studies with a total of 349 patients were included. A total of 262 (75%) played football, 37 (11%) played baseball, 19 (5%) played rugby, 10 (3%) played basketball, 10 (3%) played hockey, 9 (3%) were wresters, and 2 (1%) played soccer. ACDF was reported in 13 studies, PF in 3 studies, and CDR in 2 studies. The majority of studies suggest that RTP after surgical management is safe in elite athletes who are asymptomatic after their procedure and may lead to higher rates and earlier times of RTP. There is limited evidence regarding RTP or outcomes after CDR or multilevel surgery. CONCLUSION: The management and RTP in elite athletes after cervical spine injury is a highly complex and multifactorial topic. The overall evidence in this review suggests that RTP in asymptomatic athletes after both ACDF and PF is safe, and there is little evidence for decreased performance postoperatively. Surgical management results in a higher RTP rate compared with athletes managed conservatively.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".