The Burden of Plastic Surgery Related Disease in Canada: A Perspective Based on the 2019 Global Burden of Disease Study
Bibliographic record
Abstract
Purpose: Identifying the burden of disease related to plastic and reconstructive surgery in Canada will provide timely population-based data, inform policy, and generate support for research funding. Methods and Patients: Data on the burden of disease (ie, prevalence, incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years [DALYs]), were extracted from the Global Burden of Disease 2019 results tool for all available and relevant plastic surgery diseases. The economic burden of disease in Canadian dollars was calculated based on prior studies. Data are presented as either rates (per 100 000) or counts with the associated uncertainty interval. Results: In 2019, plastic surgery related conditions in Canada had an overall age-standardized DALY rate of 556 per 100 000 [463-664]. Of these conditions, breast cancer was responsible for approximately 50% of the overall burden of disease, with an age-standardized DALY rate of 268 per 100 000 [244-294] followed by squamous cell carcinoma (66 per 100 000 [45-94]) and thermal burns (61 per 100 000 [46-82]). Age-standardized incidence rates were highest for cellulitis (2654 per 100 000 [2502-2812]). Breast cancer had the highest age-standardized cost of care of all plastic surgery related diseases, at $5.1 billion, approximately half of the total age-standardized cost of $10.6 billion for included plastic surgery diseases. Conclusion: Plastic and reconstructive surgery related diseases, particularly breast cancer, thermal burns, and malignant melanoma, are responsible for a high burden of disease and significant cost to the Canadian healthcare system. These results will help guide national healthcare policy and should provide support to directing funding and research efforts toward impactful diseases facing the Canadian healthcare system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".