The impact of targeted therapy on healthcare resource use in patients with metastatic renal cell carcinoma: The University of Sherbrooke experience
Bibliographic record
Abstract
INTRODUCTION: We assessed the impact of targeted therapies on healthcare resource use and compared treatment regimens used in patients diagnosed with metastatic renal cell carcinoma (mRCC). METHODS: Clinicopathological and administrative data of patients with mRCC from our institution were retrospectively collected from January 2000 to August 2014. Patients were divided into two groups based on the use of targeted therapies. Healthcare resource use (HCRU) data included non-scheduled total number of hospitalizations, total days hospitalized, emergency department visits, and healthcare professional consultations. Each variable was presented with absolute and relative values (i.e., per month of survival). Statistics relied on the use of t-student and Chi-square tests. RESULTS: Ninety-nine patients were included in the study; 60 were treated with targeted therapy. There were no statistically significant differences between the two groups for demographic features and clinicopathological stage. HCRU analysis revealed an absolute increase in the median number of healthcare consultants (6 vs. 4; p<0.01) and emergency department visits (1 vs. 0; p=0.02) for the targeted therapy group. However, analysis per month of survival showed the targeted therapy group had fewer consultants (0.33 vs. 0.40; p=0.04) and hospitalizations (0.09 vs. 0.13; p=0.03) than their counterpart. Population size, non-randomization, treatment selection bias, and heterogeneity were the main limitations of this study. CONCLUSIONS: Monthly use of HCRU is lower in mRCC patients treated with targeted therapies. However, because of a greater overall survival, their absolute total HCRU will be higher than those not exposed to targeted agents.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".