Sternoclavicular Joint Reconstruction with Semitendinosus Allograft and Suture Anchors after Recurrent Posterior Dislocation in a Professional North American Football Player
Bibliographic record
Abstract
Background: Posterior sternoclavicular joint dislocations are an extremely rare but potentially life-threatening injury that can occur in sports. A variety of surgical procedures have been proposed, but there is no consensus on the treatment of choice. It is also largely unknown if a safe return to high-risk sports is possible. Case Presentation: We present a case of a posterior sternoclavicular joint dislocation in a 22-year-old male professional North American football player who had a recurrent irreducible posterior dislocation after initial injury management by closed reduction. The patient’s desire to return to football presented unique challenges to management. His sternoclavicular joint was subsequently reconstructed with semitendinosus allograft in a figure-of-eight augmented with suture anchors. After recovery, he returned to play as a running back in professional football symptom-free. Conclusion: Our patient's successful return to playing professional football after the sternoclavicular joint reconstruction suggests that this should be considered an effective treatment option when managing posterior sternoclavicular dislocation in high level contact sports players.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".