Practice gaps and challenges integrating new immuno-oncology agents in the treatment of cancer patients in the United States: A mixed-method study.
Bibliographic record
Abstract
11028 Background: Previous research has indicated challenges integrating new immuno-oncology agents (IOAs) and predictive immune biomarkers into practice. Barriers, clinical gaps and underlying causalities explaining these challenges, however, are poorly understood. Methods: A mixed-methods educational needs assessment was conducted with physicians from 6 specialties (oncology, interventional radiology, pathology, pulmonology, emergency medicine and rheumatology), clinical pharmacists, physician assistants and advanced nurse practitioners involved in the care of cancer patients in the United States. Semi-structured interviews and discussion groups were thematically analyzed to identify challenges, barriers and underlying causalities. Qualitative findings subsequently informed the development of online surveys, which served to quantify findings. The following findings pertain to oncologists. Results: A total of 660 health care providers participated in the study, in which 17 interviews and 88 surveys were completed with oncologists. Seventy-two percent reported sub-optimal knowledge of the interactions between IOAs and the tumor’s micro-environment, while 62% reported sub-optimal skills determining which IOA to select based on this information. Oncologists reported sub-optimal knowledge of best practices for using IOAs to treat cancer in presence of an autoimmune disease (74%-80% depending on condition), and sub-optimal skills weighing the risks and benefits of prescribing IOAs for these profiles (66%-77%). In addition, 50% of oncologists reported feeling overwhelmed by the volume of new IOAs being made available. Many oncologists expressed doubts regarding the clinical benefit (59%) and innovative nature (43%) of emerging IOAs. Finally, 46% reported limited skills identifying viable treatment options based on pharmacodiagnostic test reports. Barriers to having predictive biomarkers inform treatment decisions included sub-optimal communication between specialists regarding specimen requirements and desired biomarker information. Conclusions: This study demonstrates the need to further support healthcare professionals as they face challenges integrating new IOAs and predictive immune biomarkers into practice. Given the wide array of IOAs becoming available each year, addressing the knowledge, skills, confidence and attitude gaps identified in this study could help improve health care delivery and potentially optimize outcomes for cancer patients.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".