Percutaneous Cementoplasty to Treat Sternal Instability After Cardiac Surgery
Bibliographic record
Abstract
Introduction Although rare, sternal pseudarthrosis is encountered after cardiac surgery and impacts the quality of life by triggering motion-dependent chest pain. We thought to describe its treatment by percutaneous cementoplasty and report the clinical follow-up of patients treated in our institution. Methods This case series is a retrospective study based on five patients who benefited from a sternal cementoplasty as a treatment for symptomatic pseudarthrosis after cardiac surgery. The progression of the symptoms was assessed during clinical follow-up using the Quebec back pain disability (QBPD) scale and Visual Analog Scale (VAS). Results None of the patients presented evidence of local complications or neurological disorders. The intra- et post-operative images show no major leak of the cement, no embolism and no damage to the internal mammary artery or the heart. All patients described an improved quality of life due to reduced pain in all-day clinical activities. The QBPD scores improved from 54.8 ± 29.3 to 30.0 ± 17.4 (p = 0.02) and the VAS from 7.0 ± 2.8 to 1.6 ± 1.6 (p = 0.01). Furthermore, three out of five patients could completely stop taking analgesics. Conclusion Sternal pseudarthrosis is a debilitating affliction that may complicate sternotomy after cardiac surgery. This series demonstrates that a more conservative approach such as cementoplasty can be successful in terms of reducing pain, and constitutes a promising technique in selected cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".