Clinical practice patterns and cost effectiveness of human epidermal growth receptor 2 testing strategies in breast cancer patients
Bibliographic record
Abstract
BACKGROUND: Testing technologies are increasingly used to target cancer therapies. Human epidermal growth factor receptor 2 (HER2) testing to target trastuzumab for patients with breast cancer provides insights into the evidence needed for emerging testing technologies. METHODS: The authors reviewed literature on HER2 test utilization and cost effectiveness of HER2 testing for patients with breast cancer. They examined available evidence on: percentage of eligible patients tested for HER2; test methods used; concordance of test results between community and central/reference laboratories; use of trastuzumab by HER2 test result; and cost effectiveness of testing strategies. RESULTS: Little evidence was available to determine whether all eligible patients are tested, how many are retested to confirm results, and how many with negative HER2 test results still receive trastuzumab. Studies suggested that up to 66% of eligible patients had no documentation of testing in claims records, up to 20% of patients receiving trastuzumab were not tested or had no documentation of a positive test, and 20% of HER2 results may be incorrect. Few cost-effectiveness analyses of trastuzumab explicitly considered the economic implications of various testing strategies. CONCLUSIONS: There was little information about the actual use of HER2 testing in clinical practice, but evidence suggested important variations in testing practices and key gaps in knowledge exist. Given the increasing use of targeted therapies, it is critical to build an evidence base that supports informed decision making on emerging testing technologies in cancer care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".