Resource utilization and cost efficacy analysis of dose-dense methotrexate, vinblastine, doxorubicin, and cisplatin (DD-MVAC) versus gemcitabine-cisplatin (GC) as neoadjuvant chemotherapy (NAC) for muscle invasive bladder cancer (MIBC).
Bibliographic record
Abstract
e19390 Background: DD-MVAC and GC are commonly used NAC regimens for MIBC. While efficacy across studies appears similar, resource utilization (RU) burden and cost efficacy have not been compared. Methods: We assessed RU and cost effectiveness of NAC with GC vs DD-MVAC among MIBC patients (pts) treated at Dana-Farber. Data on chemotherapy administered, supportive medications, relevant procedures, hospitalizations, clinic, infusion, and emergency room (ER) visits were collected retrospectively. Unit costs for each RU component were sought from the Centers for Medicare and Medicaid Website as well as relevant published sources. Utilization was compared between MVAC and GC using multivariate quantile regression (QR) analysis. Results: 147 pts were included; 51 received DD-MVAC and 86 GC. Baseline characteristics were similar, except lower mean age (59 vs 67 years, p < 0.001) and higher proportion of ECOG-PS = 0 (96.1% vs 60.5%, p < 0.001) for DD-MVAC. The mean cumulative cisplatin dosage was similar (DD-MVAC = 284 mg/m2, GC = 257 mg/m3). More DD-MVAC pts required G-CSF analogues (100% vs 32.6%, p < 0.001), central line placement (28.6% vs 11.8%, p = 0.017), and ER visits (35% vs 18%, p = 0.048). Infusion visits (12 vs 8/pt) and cardiac imaging (0.98 vs 0.58/pt, p < 0.001) were higher for DD-MVAC, whereas GC pts required more frequent clinic visits (mean of 9 vs 5/pt), chemotherapy cycle delays (30.2% vs 9.8%, p = 0.008) and hospitalization days (mean of 0.88 vs 0.49/pt). After adjusting for PS, the mean total cost/pt was higher for DD-MVAC ($17360 vs $12112, p < 0.001). Age was not statistically significant in the QR model (p = 0.628). Conclusions: DD-MVAC and GC exhibit different RU characteristics as NAC for MIBC. Although excess RU did not clearly favor one regimen, adjustment for PS indicated significant decrease in healthcare costs by approximately 30% using GC compared to DD-MVAC. Given that similar overall delivery of cumulative cisplatin dosage was feasible with both regimens, the values and costs affixed to different resources may impact the selection of DD-MVAC vs GC. Limitations were retrospective design and costs being specific to the US.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".