The Experience of a Multidisciplinary Clinic in the Management of Early-Stage Breast Cancer Patients Receiving Trastuzumab Therapy: An Observational Study
Bibliographic record
Abstract
Background. We established a dedicated cardiac oncology clinic in 2008 for the rapid diagnosis and treatment of cardiotoxicity related to cancer therapy. In this retrospective observational study, we report on clinical outcomes in women with early-stage breast cancer (EBC) referred to this clinic. Methods. Patients with EBC treated with chemotherapy/trastuzumab and referred between October 2008 and December 2010. Data included patient demographics, staging, cancer treatment/completion, dose delays, left ventricular ejection fraction (LVEF) and cardiac treatment. Results. Forty eight patients: median age 55.5 years, stage I/II disease (77%) and HER-2 positive (98%). The majority of women (n = 32) were referred for decreases in LVEF (from baseline). Overall, 37 (77%) patients experienced at least one drop in LVEF while on treatment, of which 22 patients (59%) experienced a ≥10 percentage point drop. The majority of patients (30/37; 81%) experienced declines in LVEF while on trastuzumab. Interventions included trastuzumab delays (n = 16/48; 33%) and cardiac medication (12/48: 25%). A total of 81% of patients completed ≥90% of trastuzumab therapy and 15% of patients discontinued therapy due to cardiotoxicity. Conclusion. The majority of patients referred to our clinic completed therapy. Further studies are needed to determine the impact of this multidisciplinary approach on treatment completion and cardiac outcomes.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".