Singapore Cancer Network (SCAN) Guidelines for Adjuvant Trastuzumab Use in Early Stage HER2 Positive Breast Cancer
Bibliographic record
Abstract
INTRODUCTION: The SCAN breast cancer workgroup aimed to develop Singapore Cancer Network (SCAN) clinical practice guidelines for adjuvant trastuzumab use in early stage HER2 positive breast cancer. MATERIALS AND METHODS: The workgroup utilised a modified ADAPTE process to calibrate high quality international evidence-based clinical practice guidelines to our local setting. RESULTS: Five international guidelines were evaluated- those developed by the National Comprehensive Cancer Network (2015), the National Institute of Health and Clinical Excellence (2006, 2009), the European Society of Medical Oncology (2013), the Breast Cancer Disease Site Group in conjunction with the Program in Evidence Based Care and Cancer Care Ontario (2011) and the Scottish Intercollegiate Guidelines Network (2013). Recommendations on suitable candidacy for adjuvant trastuzumab, whether adjuvant trastuzumab should be given concurrently with a taxane or sequentially after completion of adjuvant chemotherapy, the optimal frequency of cardiac monitoring during adjuvant trastuzumab and the optimal duration of adjuvant trastuzumab were developed. CONCLUSION: These adapted guidelines form the SCAN Guidelines 2015 for adjuvant trastuzumab use in early stage HER2 positive breast cancer.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".