Abstract PS14-13: National comprehensive cancer network (NCCN) recommendations for drugs without US food and drug administration (FDA) approval in metastatic breast cancer: A cross-sectional study
Bibliographic record
Abstract
Abstract Background: NCCN guidelines include recommendations not approved by the FDA. We aimed to compare the NCCN recommendations for metastatic breast cancer (MBC) with the FDA approved indications and to identify characteristics that are associated with NCCN recommendations for off-label treatment. Methods: All NCCN recommendations for MBC and their supporting data were identified. Drug labels were reviewed to determine whether recommendations are FDA approved indications. Odds ratio (OR) and 95% confidence interval (CI) were calculated to compare between FDA approved and off-label recommendations for pre-specified categories including drug type, tumor subtype, level of recommendation and line of therapy. Results: Of 124 recommendations identified, 68 (55%) were off-label. Chemotherapy and human epidermal growth factor receptor 2 (HER2) targeted drugs were associated with less frequent approved indications (OR=0.28, 95% CI 0.12-0.62, p=0.001 and OR=0.29, 95% CI 0.12-0.70, p=0.005, respectively). Recommendations for endocrine therapy (OR=3.44, 95% CI 1.32-9.09, p=0.009) and non-HER2 targeted treatment (OR=10.0, 95% CI 3.03-33.33, p<0.001) were associated with FDA approved indications. Compared to combination therapies, monotherapies were more likely to be FDA approved (OR=3.45, 95% CI 1.64-7.23, p=0.001). Category 1 (OR=7.63, 95% CI 2.19-26.55, p=0.001) and preferred NCCN recommendations (OR=5.49, 95% CI 2.36-12.77, p<0.001) were more likely to be FDA-approved indications. Compared to off-label recommendations, NCCN recommendations of approved drugs were based on significantly higher sample size (mean 477 vs. 342 patients, p=0.017) and were non- significantly associated with availability of randomized data (OR=1.92, 95% CI 0.87-4.24, p=0.10).Conclusion: More than half of all NCCN recommendations for MBC are off-label, mostly involving chemotherapy containing regimes for HER2 negative disease and combinations which include HER2 targeting drugs. The clarity of the NCCN guidelines can be improved by underlining the strength of the evidence supporting various recommendations for MBC and the hierarchy between various treatment options. Citation Format: Tal Etan, Eitan Amir, Adriana Tibau, Rinat Yerushalmi, Assaf Moore, Daniel Shepshelovich, Hadar Goldvaser. National comprehensive cancer network (NCCN) recommendations for drugs without US food and drug administration (FDA) approval in metastatic breast cancer: A cross-sectional study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS14-13.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".