Adjuvant trastuzumab (T) therapy in HER2+ breast cancer after ASCO 2005: Patients’ attitudes and immediate economic burden
Bibliographic record
Abstract
10565 Background: Data from 3 large randomized trials documenting the efficacy of T in the adjuvant setting were reported at ASCO 2005, and subsequently published in NEJM 2005; 353: (16) pp 1659–72 and 1673–1684 . We decided to offer T, and attempted to assess patients’ characteristics that influence its acceptance, in a subgroup of HER2 + patients that had already completed adjuvant chemotherapy at our institution within 12 months prior to these reported results. Methods: Using Electronic Medical Records (OpTx, Canada), we identified HER2+ breast cancer patients who had completed adjuvant therapy within the prior 12 months and administered an informational synopsis about the study results. They then completed a questionnaire, including their demographic information, that established their understanding of the data and documented their decision to receive or to not receive adjuvant T as an afterthought. Results: We identified 1442 breast cancer patients in Optx that were seen at UTCI for initial or follow up visits between May 2004 and May 2005. Those with 3 or fewer visits within the last year and those who received no chemotherapy (n = 770) were excluded. Of the remaining 672 patients, only 104 (15%) had documented HER2+ disease. Fourteen HER2+ patients had metastatic disease, while 84 patients, though HER2+, had either completed adjuvant chemotherapy greater than 12 months prior or were currently receiving adjuvant therapy or T, and/or had other reasons to not be suitable for T. Six patients qualified for this study; 5 decided to receive adjuvant T and 1 chose not to because she perceived the additional benefit to be minimal. Conclusions: While our sample size was too small in the end to draw conclusions about patients’ attitudes towards new data on adjuvant T, we were struck by the small number of patients who could be offered T as an afterthought despite our large patient volume. The magnitude of the perceived economic burden and its imminence after the release of these data may have been overestimated. [Table: see text]
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".