A Comparison of Patients’ and Physicians’ Knowledge and Expectations Regarding Precision Oncology Tests
Bibliographic record
Abstract
(1) Background: As genomic testing is becoming a part of the mainstream oncology practice, it is vital to ensure that our patients fully understand the implications of these tests. This study aimed to compare the attitudes and expectations of cancer patients with those of their physicians regarding the role of biomarker testing in clinical decision making. (2) Methods: Two separate, complimentary, self-administered questionnaires for patients with cancer and their physicians, respectively, were collected in Calgary, Alberta, Canada. Out of 117, 113 completed patient surveys were included in the statistical analysis, constituting a 96.4% response rate. These surveys were subsequently matched with those of their corresponding oncologists to determine the concordance rates. (3) Results: Overall, patients demonstrated a good understanding of general cancer biology (80.0%) and diagnostic processes (90.0%) associated with precision oncology. Most patients wanted their tumours to be tested to guide treatment, and the oncologists broadly shared these views (concordance 65.1%). However, there were discrepancies between the knowledge and expectations regarding the applications of test results on actual diagnosis and prognosis between patients and their oncologists (concordance 26.1% and 36.0%, respectively). While only 28.0% of patients thought they had enough knowledge to make informed decisions, the majority (68.0%) said they needed more information. (4) Conclusion: Our study shows that patients and cancer physicians do not always agree with the roles and applications of genomic tests, which could lead to misplaced expectations and poor health outcomes. More research is needed to devise strategies to improve education and communication to align these expectations and improve the quality of clinical decision making.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".