Understanding communication between patients and healthcare professionals regarding comprehensive biomarker testing in precision oncology: A scoping review
Bibliographic record
Abstract
BACKGROUND: Precision oncology, using comprehensive biomarker testing (cBT) to inform individual cancer diagnosis, prognosis and treatment, includes increasingly complex technology and clinical data sets. People impacted by cancer (patients and caregivers) and healthcare professionals (HCPs) face distinct challenges in navigating the cBT and personalized treatment landscape. This review summarizes evidence regarding cBT-related communication between people impacted by cancer and HCPs and identifies important avenues for future research in precision oncology. METHODS: A scoping review was conducted using records published in PubMed during January 2017-August 2022, focusing on the breadth of topics on patient-HCP communication and knowledge resources used by HCPs as guidance in cBT-related communication. Data were extracted from records meeting inclusion criteria, and findings were summarized according to main topics. RESULTS: The search identified 287 unique records and data were extracted from 42 records, including nine from expert input. Most records originated from the United States included patients with different types of cancer, and oncologists were the main HCPs. Patients' motivation for undergoing cBT and receiving results was generally high in different settings. However, patients' understanding of cBT-related concepts was limited, and their knowledge and information preferences changed based on cBT implications and significance to family members. HCPs were valued by patients as a trusted source of information. Limited evidence was available on HCPs' information-seeking behavior and factors influencing cBT-related knowledge and confidence, often self-reported as insufficient. CONCLUSIONS: Patient education by knowledgeable and confident HCPs, information management and a caring patient-HCP relationship communicating continuity of care regardless of cBT results are crucial to empower patients and shared decision-making in precision oncology. More data on the process and structure of cBT-related communication, distinction between and characterization of different timepoints of patient-HCP interactions are needed.
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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 | 0.035 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".