Diagnostics and treatment for HER2+ mG&CRC: Learning outcomes of GetSMART.
Bibliographic record
Abstract
82 Background: Gastric/colorectal cancer (G&CRC) are among the most prevalent cancers. Efforts are ongoing to better assess their clinical/genomic factors. HER2 (human epidermal growth factor receptor 2) testing is strongly recommended in clinical practice to guide treatment. An accredited education program (GetSMART) was launched online (January 2021–April 2022) to equip healthcare providers (HCPs) with knowledge, skills, and confidence to accurately assess/apply molecular testing and therapy options for metastatic G&CRC patients. Methods: A knowledge/confidence-based assessment was embedded in four modules to measure learners’ knowledge mastery (% answering correctly with confidence) pre/post-activity. Self-reported confidence and anticipated performance in clinical practice were measured before/after each module. At the end of program, HCPs were asked about their commitment to change. McNemar’s test was applied to assess statistical significance in pre-post for paired items, and descriptive analysis for non-paired items. Results: Overall, 925 unique HCPs engaged in GetSMART, 31% (n=284) completed all modules, of which 57% (n=163) consented analysis of their data. A statistically significant increase (p<0.05) was measured in percentage of HCPs demonstrating knowledge mastery, confidence, and commitment to change their practice from pre to post across four competencies, except for percentage of HCPs “often”/“always” engaging patients in shared decision-making (SDM) (3, Performance). Largest gains in knowledge mastery/confidence were noted for HCPs’ ability to anticipate/manage adverse events (AE) ( 4A); and for anticipated performance, in HCPs considering appropriate treatment. Post-activity, 66% (98/149) of HCPs intended to change practice based on module content, 83% (70/84) of whom were “highly”/“somewhat” confident to make changes. The activity’s anticipated impact on patients was rated “major”/“moderate” by 69% (103/149) of HCPs. Conclusions: Identification of HER2 aberrations in mG&CRC and its appropriate treatment selection remains challenging in oncology care. This evaluation demonstrated that GetSMART positively impacted HCPs’ knowledge, confidence, and anticipated performance in clinical practice to enhance health outcomes of HER2+ mG&CRC patients. [Table: see text]
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".