Clinical effectiveness of trastuzumab: early experience
Bibliographic record
Abstract
Objective. To assess the clinical effectiveness of trastuzumab among metastatic breast cancer (MBC) patients and compare the results to those reported in the two pivotal clinical trials and product monograph. Design. Retrospective chart review of all patients who had initiated trastuzumab monotherapy or combination therapy for MBC within the Alberta Cancer Board (ACB) from August 1998 to May 2001. Setting. Two public tertiary cancer centres in the Canadian province of Alberta. Patients. Of 90 patients reviewed within the ACB, 72 women were eligible for the study. Main outcome measures. The primary endpoints measured were time to treatment failure (TTF) and survival. Secondary end-points measured included adverse events and compliance with ACB guidelines for trastuzumab administration. Results. Among all 72 patients, median TTF was 7.6 months and median survival was 14.4 months. Trastuzumab combination therapy was associated with a significantly longer median TTF compared to trastuzumab monotherapy (P= 0.011). With respect to survival, no significant advantage was seen with combination therapy over monotherapy (P= 0.438). Infusion-related reactions were reported in 11.1% of our patients, while cardiotoxicity was reported in12.5%. Conclusions. Overall, we found that trastuzumab performs better in the clinical setting than it did in the pivotal trials with respect to TTF (2.5 vs. 2.4 months and 8.2 vs. 6.9 months), but not as well with respect to survival (10.0 vs. 13.0 months and 21.0 vs. 25.1 months). In comparison to the product monograph, we report a significantly lower incidence of infusion-related reactions and a slightly higher incidence of cardiotoxicity.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".