Reflecting the Thoracic Fellowship in Canada as a Japanese Thoracic Surgeon: Is There Anything We Should Follow?
Bibliographic record
Abstract
In Japanese surgical society, there have been urgent discussions as to the decrease in the number of junior doctors who want to be surgical specialists. This problem seems to have originated from the loss of attractiveness of surgery. One of the counter-measures to regain the attractiveness of surgical specialties might be a well-organized training system, for which the Japanese Board of General Thoracic Surgery (JBGTS), as well as those of other surgical subspecialties, has struggled. Fortunately, I had an opportunity of general thoracic surgery training in Canada, and had a chance to reflect on the thoracic training programs of both countries. Based on my experience as a thoracic fellow in Canada, I would like to introduce the Canadian way of thoracic surgery training, referring to the differences between each program.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".