Training in cardiac surgery using human cadavers: Effectiveness of “Silent Teachers”
Bibliographic record
Abstract
BACKGROUND: Surgical skills acquisition in cardiac surgery requires consistent and hard practice. Furthermore, training using cadaver is advocated as a means of transferring learned skills to the operating room and recreate surgical situations for trainees to practice and hone their skills. We expose our experience in training for cardiac surgical procedures using human cadavers. METHODS: From June 2013 to November 2016, we performed 302 cardiac surgical procedures on 50 human cadavers obtained according to the Ivorian laws in force. Cadavers were preserved in 10% formaldehyde and by cryopreservation. RESULTS: In open heart, cardiac surgical techniques were achieved via sternotomy (n = 24) or via "lid-anterolateral thoracotomy" (n = 2). Pericardotomy (n = 26) and/or pericardiectomy (n = 26) were systematic. Aortic and caval canulations and pulmonary artery control (n = 30) were performed. After cardiotomy and arterial incisions (n = 34), 18 atrial and ventricular septal defects repair, 1 Fontan operation, 1 arterial switch, 11 enlargement procedures of the whole right ventricular outlet and 15 acquired valve heart diseases corrections were performed. In closed-heart surgery, procedures were achieved via sternotomy (n = 7), posterolateral thoracotomy (n = 12), or Marfan retroxiphoid approach (n = 3). Pericardotomy (n = 7) or pericardiectomy (n = 7) were performed. Great vessels dissections and expositions (n = 21) were achieved to perform 4 pulmonary artery bandings, 12 patent ductus arteriosus closures, 3 Waldhausen procedures, 7 Brock Operations, and 2 Blalock-Taussig shunts. In both situations, 29 direct pulmonary arterial, auricular, and ventricular sutures were achieved. CONCLUSION: Surgical simulation in cadaver models offer an opportunity for trainees to practice their surgical skills before entering operating room.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".