Improvement of sickle cell disease care mitigates the healthcare utilization induced by increased prevalence: Experience of a tertiary pediatric center
Bibliographic record
Abstract
ABSTRACT Background Sickle cell disease (SCD) has undergone major changes in the last decades. Its prevalence has been steadily increasing and numerous advances have been made in the management of the disease. However, the effect in real‐life setting of these major changes is unknown, particularly in a Canadian environment. Procedure We aimed to assess the impact of these changes on the evolution in the healthcare utilization (HCU) of children with SCD in a Canadian pediatric tertiary center from 2009 to 2024. Results The number of children with SCD followed at our center more than doubled (221 to 499 patients). Practice changes reduced mean time to hydroxyurea introduction, resulting in a steady annual increase in mean fetal hemoglobin across the patients with HbSS. Reflecting the number of patients followed, the absolute number of annual outpatient and ED visits increased significantly (1207 to 1769 and 192 to 526, respectively). However, there was a significant decrease in the mean number of outpatient visits (5.46 to 3.55 [ p = 0.03]) and hospitalizations (1.18 to 0.58 [ p <0.0001]) by patient annually. There was also a reduction in the percentage of admissions after an ED visit (62.5% to 42.6% [ p <0.0001]). Conclusion Although the number of patients with SCD followed at our institution drastically increased in 15 years, the practice changes were effective and likely mitigated the impact on admissions. It illustrates the significant impact of improved management in the care of patients with SCD. Allocated resources need to reflect the overall increase in HCU to allow for continuous optimal care of this vulnerable 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".