The Changing Epidemiology of Pediatric Hemoglobinopathy Patients in Northern Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Hemoglobinopathies are associated with significant morbidity and mortality. Accurate epidemiologic data reflecting the number of hemoglobinopathy patients are lacking in Canada. Immigration patterns are shifting such that regions where these diseases were rare are seeing a rapid population expansion, revealing a gap in the health care system and the need for a public health response. METHODS: To understand the epidemiology of pediatric hemoglobinopathy patients given the provincial population growth and immigration patterns, a retrospective chart review was conducted at the Stollery Children's Hospital from January 2004 to July 2014. RESULTS: A total of 88% of patients had sickle cell disease; 55% of patients were Canadian born and 63% of families originated from Africa. There was a 3.5-fold increase in patient numbers with acceleration in patient accrual over the study period and a delay in diagnosis in 70% of patients. There was a significant increase in the number of hospitalizations over the study period. Thirteen percent required at least 1 exchange transfusion, 16% received chronic transfusions, and 30% of patients developed at least 1 severe complication related to their diagnosis. CONCLUSIONS: It is imperative to demonstrate the growing hemoglobinopathy population and changing health care requirements to advocate for appropriate resources, educate health care providers, and increase awareness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| 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".