Association between genetic information provision and decisional conflict in patients with cancer.
Bibliographic record
Abstract
227 Background: The decisional conflict scale (DCS) measures personal perception of uncertainty when facing a decision. In patients with cancer, decision conflict is likely when considering uncertainty in oncologic systemic treatment outcomes. Cancer genomic information can introduce more uncertainty and complexity in care. This study aims to evaluate patients’ DCS score pre or post discussing next generation sequencing (NGS) results with a medical oncologist, and assess factors that correlate with high decision conflict. Methods: Patients diagnosed with incurable/metastatic cancer who underwent molecular characterization with tissue+/- liquid NGS testing were enrolled. Survey instruments included the validated DCS (five subscales: informed, values clarity, support, uncertainty, effective decision), and EQ-5D-5L. DCS was administered pre or post medical oncology consultation that included review of tissue +/- liquid NGS results and treatment options. Higher DCS score correlates with higher perception of uncertainty. Descriptive statistics were used to assess clinical and demographic risk factors. Multivariable logistic regression analysis estimated the correlation between high DCS and clinical factors. Results: 335 DCS surveys were completed by 227 patients: 188 before and 147 after discussing tissue+/- liquid NGS results with a medical oncologist. Baseline characteristics: 56% female, median age 65, ECOG 0-1 58%, median EQ5D VAS score 68, GI/lung/gyne/breast/other 42/32/10/5/11%, white/Asian/other/unknown 66/12/5/17%. Tier 1 variants were identified in 35% of patients. Patients reported decreased decision conflict after consultation. Multivariable logistic regression analysis including sex, age, race and tumor group did not predict for high DCS pre-consultation. MVA post NGS results that also included Tier 1 variants (present / absent) did not predict for high DSC post consultation. Conclusions: Communication regarding genomic-based tumor assessment can positively support patients in decision conflict, and improve their confidence in their treatment choice, regardless of the findings of the report. Low DCS after consultation supports the goal of shared decision making for cancer treatment. Clinical trial information: NCT05057234 . DCS subscale Pre-consultation Post-consultation p value Informed 49.3 37.5 <0.001 Values clarity 45.3 35.5 <0.001 Support 34.4 28.2 0.005 Uncertainty 46.8 36.1 <0.001 Effective decision 40.0 31.4 <0.001 TOTAL 42.9 33.6 <0.001
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".