Education and employment status of children and adults with thalassemia in North America
Bibliographic record
Abstract
BACKGROUND: Advances in the management of thalassemia have resulted in increased life expectancy and new challenges. We conducted the first survey of education and employment status of people with thalassemia in North America. PROCEDURES: A total of 633 patients (349 adults and 284 school age children) enrolled in the Thalassemia Clinical Research Network (TCRN) registry in Canada and the U.S. were included in the data analysis. Predictors considered for analysis were age, gender, race/ethnicity, site of treatment (Canada vs. United States), transfusion and chelation status, serum ferritin, and clinical complications. RESULTS: Seventy percent of adults were employed of which 67% reported working full-time. Sixty percent had a college degree and 14% had achieved some post-college education. Eighty-two percent of school age children were at expected grade level. In a multivariate analysis for adults, Whites (OR = 2.76, 95% CI: 1.50-5.06) were more likely to be employed compared to Asians. Higher education in adults was associated with older age (OR = 1.67, 95% CI: 1.29-2.15), female gender (OR = 2.08, 95% CI: 1.32-3.23) and absence of lung disease (OR = 14.3, 95% CI: 2.04-100). Younger children (OR = 5.7 for 10-year increments, 95% CI: 2.0-16.7) and Canadian patients (OR = 5.6, 95% CI: 1.5-20) were more likely to be at the expected education level. Neither transfusion nor chelation was associated with lower employment or educational achievement. CONCLUSIONS: Individuals with thalassemia in North America can achieve higher education; however, full-time employment remains a problem. Transfusion and chelation do not affect employment or education status of this patient population.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".