The economic burden of pleural effusions in patients with chronic myeloid leukemia treated with tyrosine kinase inhibitors
Bibliographic record
Abstract
OBJECTIVE: Tyrosine kinase inhibitors (TKI), the standard of care for patients with chronic myeloid leukemia (CML) patients, may in some cases lead to the development of pleural effusion (PE). The purpose of this study is to compare healthcare resource utilization and costs associated with PE among CML patients treated with a TKI therapy. METHODS: Two large retrospective claims databases (1999-2009) were combined to identify adult CML patients who received ≥1 TKI prescription before the index date, which was defined as 30 days before the first PE diagnosis for patients with PE and a randomly selected date for PE-free patients. Patients were followed for 6 months after the index date. PE and PE-free patients were matched on a 1:1 ratio. PE-related resource utilization and costs (measured in 2009 US dollars) were estimated for PE patients. All-cause and CML-related resource utilization and costs were compared between PE and PE-free patients. Multivariate regression models were used to control for confounding factors. RESULTS: The study included 186 matched pairs. PE-free and PE patients were on average 65.4 and 63.6 years old and 39.8% and 48.9% were female, respectively. PE patients had a significantly higher number of inpatient (IP) days, IP admissions, outpatient (OP) visits and emergency room (ER) visits than PE-free patients (all p < 0.01). All-cause medical services costs were $88,526 and $30,434 for PE and PE-free patients, respectively. After adjusting for confounding factors, the PE-related total medical costs were $47,288 (p < 0.01), which was mostly accounted for by higher IP (difference: $34,123, p < 0.01) and OP (difference: $9563, p < 0.05) costs. PE patients also incurred higher CML-related medical costs compared to PE-free patients (difference: $39,599; p < 0.01). CONCLUSION: PE presents a substantial economic burden for CML patients treated with TKI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".