Mortality from acquired bullous diseases of skin in Canadian adults 2000–2007
Bibliographic record
Abstract
BACKGROUND: Bullous skin diseases are known to be associated with significant morbidity and mortality. There have been no studies on mortality from severe bullous skin diseases in Canada. METHODS: We used mortality data from the Statistics Canada website from 2000 to 2007 for three major bullous skin diseases: bullous pemphigoid; pemphigus; and toxic epidermal necrolysis (TEN). Crude and age-standardized mortality rates were calculated and compared with the corresponding US mortality rates. Linear regression was used to assess time trend and effect of gender and age on mortality rates. RESULTS: During the period of eight years, there were 115 deaths attributed to pemphigoid, 84 to pemphigus, and 44 to TEN. The crude annual mortality rate was the highest for pemphigoid (0.045 per 100,000), followed by pemphigus (0.033), and TEN (0.017). None of these conditions demonstrated significant time trends in mortality rates over the eight-year period, although a trend towards decreasing pemphigus mortality was observed (P = 0.07). No gender difference in mortality was observed, but advanced age was associated with mortality in all three conditions. CONCLUSION: Among bullous skin diseases, pemphigoid is the leading cause of mortality in Canada. This is in contrast to the USA, where TEN is the leading cause of mortality from bullous skin diseases. It is not clear whether differences in healthcare systems explain these findings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".