Patterns of trastuzumab use and cost in a single Canadian cancer institute
Bibliographic record
Abstract
6070 Background: Trastuzumab is approved for treatment of HER2/neu over-expressing metastatic breast cancer. There is an absence of data on the costs associated with trastuzumab-based treatment strategies in actual clinical practice. This study was undertaken to identify the strategies currently employed in a single Canadian cancer centre and to explore the costs associated with each strategy. Methods: We performed a retrospective review of all patients treated with trastuzumab at our centre since its approval in Canada. Data was collected using pharmacy records in conjunction with patient charts. Fifty-three patients had complete treatment data and were included in the analysis. Treatment strategies were identified based on timing of trastuzumab initiation. Mean number of cycles (1 cycle = trastuzumab weekly x 3, or Q3 weeks x 1), mean cost per patient and total cost were calculated for each strategy. Results: Four strategies were identified: first-line trastuzumab combined with chemotherapy followed by maintenance trastuzumab (T/C - T); first-line monotherapy (T); second-line or beyond combined with chemotherapy (t/c) and second-line or beyond monotherapy (t). The results are summarized in the table below. The costs reflect trastuzumab only and do not include the cost of additional chemotherapy. Only one patient received first-line monotherapy hence values in the table for “T” represent actual costs. Fifty-eight percent of patients who progressed after first-line therapy continued to receive trastuzumab after progression compared to only 33% progressing after second-line therapy. The total cost of treatment for 53 patients thus far is greater than 1.6 million dollars (Cdn.). Conclusions: Our data highlights the potential costs to be faced by health care systems as novel targeted therapies become available. It further emphasizes the importance of identifying optimal treatment strategies using these agents to ensure appropriate resource allocation. No significant financial relationships to disclose.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".