Learning Outcomes of “GetSMART,” Education for Diagnostics and Targeted Treatment for HER2+ Metastatic Gastric and Colorectal Cancers
Bibliographic record
Abstract
The treatment landscape for patients affected by gastric and colorectal cancer (G&CRC) has significantly broadened over the past decade. Molecular diagnostic methods have improved with a precision oncology-driven approach to the development of treatment options tailored to specific molecular targets, including the human epidermal growth factor 2 (HER2). While scientific evidence on the role of HER2 in G&CRC has improved, there has been a lag in general understanding and applications of testing for HER2+ G&CRC and resulting targeting treatment in the wider oncology community. To better understand and address the root causes of this gap, a needs assessment deployed among 85 oncology care providers was conducted and informed the development of an accredited online educational program entitled "GetSMART." The program consisted of four modules developed and narrated by experts in gastrointestinal oncology. The educational content and assessment metrics were guided by a confidence-based assessment (CBA) model and the Moore, Green, and Gallis outcomes framework. Assessment methods consisted of quantitative pre- and post-activity tests, an evaluation embedded within the education (n = 163), and semi-structured interviews (n = 5) post-activity completion. Findings indicated that "GetSMART" enhanced participants' knowledge, confidence, and intent to change practice in relation to their (1) identification of HER2 aberrations, (2) selection of appropriate treatments for HER2+ G&CRC, and (3) ability to engage patients in shared decision-making and management of adverse events. "GetSMART" can therefore be a valuable educational resource for oncology HCPs caring for patients affected by HER2+ metastatic G&CRC, offering strategies to ensure an optimal team and patient-centered approach to the care being delivered.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".