Age‐related treatment patterns in sickle cell disease patients and the associated sickle cell complications and healthcare costs
Bibliographic record
Abstract
BACKGROUND: This study explored the blood transfusion patterns, SCD complications, utilization of iron chelation therapies (ICT), healthcare resource use, and costs in pediatric, transitioning (18 years old) and adult patients with SCD. PROCEDURE: Data from Florida (1998-2009), New Jersey (1996-2009), Missouri (1997-2010), Kansas (2001-2009), and Iowa (1998-2010) state Medicaid were used. Patients with ≥2 SCD diagnoses and ≥1 transfusion event were included. Rates of transfusion events, SCD complications, and proportion of eligible patients receiving ICT were calculated. ICT eligibility was defined as receiving ≥10 transfusions over lifetime. SCD complications included pain, pulmonary event, infection event, renal, cardiovascular, stroke, leg ulcers, and avascular necrosis. Regressions were used to assess risk factors for transfusion and identify the main drivers of costs. RESULTS: The sample included 3,208 patients. The transfusion rate increased from 1-year-old to a peak at 16 years old, then dropped until age 26 and remained stable thereafter. In contrast the frequency of diagnoses for SCD complications increased markedly after age 16. Post-transition patients (≥18 years old) were significantly associated with fewer transfusions (odds ratio: 0.80, P = 0.002). Among eligible patients for ICT, there was no statistically significant difference in total cost between the ICT and no ICT groups (adjusted cost difference, $136, P = 0.114). CONCLUSIONS: Patients transitioning to adult care received less transfusions and hydroxyurea, less ICT when eligible for chelation therapy, had higher healthcare costs and suffered from more frequent SCD related complications than pediatric patients. These findings highlight the changes in treatment patterns corresponding to transition to adult care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".