Are We Training Plastic Surgeons for the Future Needs of the Canadian Population?
Bibliographic record
Abstract
Just over half a century ago when I was interviewed for medical school, one of the questions I was asked "In Canada, we have a problem with physicians practicing in the big cities and not in smaller communities where they are needed.What is your solution to this problem?"Without any hesitation, I responded "the solution is simple."I sensed they were surprised that a naive prospective medical student felt they knew the solution to something that was at the time a major challenge and remains one to this day."Do not give them a choice," I responded, "send them where they are needed for a prescribed period, then give them the opportunity to move elsewhere after their service if they wish.If we look to other government-funded services like the Canadian Military, where members are assigned to bases or regions where they are most needed, there is precedent-and the same could apply to physicians."Most of the cost of medical education is subsidized by the government I reasoned, so the government should be able to direct early career physician placement.Two of the three interviewers frowned at my answer.The third remained poker-faced so I was unsure what they thought of my answer.It was clear to me at the time that the panel did not care for my response, which I can understand.They raised the issues of freedom of movement and professional autonomy.Though my answer might have just ended my dream of medicine, I stood my ground.I explained that I believed it was the only fair, ethical, and democratic way to provide care for underserved areas.Half a century later, as I am entering retirement, I still find myself reflecting on this matter.From being a healthcare provider, I may soon become a healthcare consumer.Will there be a plastic surgeon available to provide me with the care I may need?Based on present trends, I am not so sure.Meaningful plastics coverage in emergencies is a serious challenge for our field.In 2007, the average waitlist time for urgent consultations was 11 days.1 I suspect it is regrettably even longer now.Where I am based (Hamilton ON), quite frequently, we do not have coverage for emergency cases in hospitals within a 20-to 50-mile radius of our university hospitals.When we confront our offending colleagues and ask why they are not seeing the patients in their catchment area, a typical
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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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.037 | 0.033 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".