A New Device for Securing Sternal Wires after Median Sternotomy
Bibliographic record
Abstract
OBJECTIVE: Morbidity due to sternotomy continues to be a significant clinical problem. Poor approximation of the sternum may lead to complications such as sternal dehiscence, infection, and pain. A device to assist in tensioning and twisting standard steel wires during sternal closure has been developed (TORQ sternal closure device). Manually tightened interrupted wire closures were compared with those tightened and secured with the aid of the device. Performance of the device was assessed clinically. METHODS: Four cardiovascular surgeons performed manual and device-assisted closures on a biofidelic model. Closure force was measured to determine the residual force and its intraoperator variation. A retrospective review of patients treated before and after the introduction of the device was conducted. Predicted and actual outcomes were compared for the two groups (manual closure and device-assisted closure). RESULTS: Biomechanical testing measured a 75% increase in residual closure force (P < 0.001) and a significant reduction in the variability of the closure force (P = 0.045) for device-assisted closures compared with manual closures. In the retrospective study, 3 of 173 manually closed patients had sterile sternal dehiscence and 1 of 173 had a deep sternal wound infection. In the device closure group, 2 of 127 had a sterile sternal dehiscence and no deep sternal wound infections were reported. No other device-related serious adverse events were reported. CONCLUSIONS: Biomechanical data showed stronger, more consistent closure forces with the device. The retrospective data attest to the performance of the device.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".