Initial clinical outcomes after completion of training in a Canadian Royal College thoracic surgery program.
Bibliographic record
Abstract
BACKGROUND: Thoracic procedures are currently performed by general and thoracic surgeons. Initial clinical outcome after training is a good measure of the quality of the surgical training received. METHODS: We examined the morbidity and mortality for pneumonectomy, lobectomy and esophagectomy during one surgeon's first 2 years of practice; we collected data prospectively. The results were based on the experience of the only dedicated thoracic surgeon (5 years of general surgery and 3 years of thoracic surgery training with certification from the Royal College of Physicians and Surgeons of Canada) at the largest tertiary care hospital of Brown University School of Medicine. RESULTS: During the 2-year period, 154 major pulmonary resections (20 pneumonectomies, 134 lobectomies) and 25 esophagectomies (18 transhiatal, 4 Ivor-Lewis, 2 thoracoabdominal, one 3-incision) were performed. Mortality for major lung resection was 1.9% (pneumonectomy 5%, lobectomy 1.5%), and morbidity was 27% (pneumonectomy 35%, lobectomy 26%). Mortality for esophagectomy was 4%, and morbidity was 36% (anastamotic leak 12%). CONCLUSIONS: These results compare favourably with clinical outcomes published from several large series. Thoracic surgical training in Canada is adequate and prepares surgeons well to perform major thoracic procedures. A database of the initial results from all graduates of thoracic surgery training in Canada is needed. Such a database could be used to compare the initial results of thoracic procedures performed by general and thoracic surgery graduates from Canada and the United States.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".