Evolution of Thoracic Surgery in Canada
Bibliographic record
Abstract
BACKGROUND: Canada’s contributions toward the 21st century’s practice of thoracic surgery have been both unique and multilayered. Scattered throughout are tales of pioneers where none had gone before, where opportunities were greeted by creativity and where iconic figures followed one another. OBJECTIVE: To describe the numerous and important achievements of Canadian thoracic surgeons in the areas of surgery for pulmonary tuberculosis, thoracic oncology, airway surgery and lung transplantation. METHOD: Information was collected through reading of the numerous publications written by Canadian thoracic surgeons over the past 100 years, interviews with interested people from all thoracic surgery divisions across Canada and review of pertinent material form the archives of several Canadian hospitals and universities. RESULTS: Many of the developments occurred by chance. It was the early and specific focus on thoracic surgery, to the exclusion of cardiac and general surgery, that distinguishes the Canadian experience, a model that is now emerging everywhere. From lung transplantation in chimera twin calves to ex vivo organ preservation, from the removal of airways to tissue regeneration, and from intensive care research to complex science, Canadians have excelled in their commitment to research. Over the years, the influence of Canadian thoracic surgery on international practice has been significant. CONCLUSIONS: Canada spearheaded the development of thoracic surgery over the past 100 years to a greater degree than any other country. From research to education, from national infrastructures to the regionalization of local practices, it happened in Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".