Diarrhea-associated hemolytic uremic syndrome in southern Alberta: A long-term single-centre experience
Bibliographic record
Abstract
BACKGROUND: Reports of long-term incidence trends of endemic diarrhea-associated hemolytic uremic syndrome (D+HUS) are few and inconclusive. OBJECTIVE: To define and analyze the incidence and outcomes of D+HUS over a period of approximately 25 years in a highly endemic region of southern Alberta. METHODS: Annual incidence rates of confirmed cases of D+HUS were compared between two 12-year periods (1980 to 1992 and 1994 to 2006). Differences in therapies used, and some short- and long-term complications observed were also compared between the two periods. RESULTS: The absolute yearly number of D+HUS cases was highly variable. The comparison between the 1980 to 1992, and 1994 to 2006 periods demonstrated a modest 8.8% decrease in the total number of cases. The population-based average annual incidence rates were not significantly different between the two time periods (3.33 cases versus 2.58 cases per 100,000 population per year, respectively; P=0.30). Only supportive care measures were used in the latter period. A mortality rate of lower than 1% in the latter period was one of the lowest ever reported for a large cohort of D+HUS patients. CONCLUSION: The present long-term retrospective study of D+HUS in a highly endemic area documented a modest decrease in the absolute number of cases but no difference in the average annual incidence over an extended period of time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".