Risk communication in genetic counseling: Exploring uptake and perception of recurrence numbers, and their impact on patient outcomes
Bibliographic record
Abstract
Providing recurrence numbers is often considered a fundamental component of genetic counseling. We sought to fill knowledge gaps regarding how often patients actively seek recurrence numbers, and how they impact patient outcomes. We conducted a retrospective chart review at a clinic where patients routinely complete the Genetic Counseling Outcomes Scale (GCOS, measuring empowerment) pre (T1)/post (T2) appointment. Using analysis of covariance, we evaluated the effect on T2 GCOS score of: (1) receiving recurrence numbers and (2) patient perception of recurrence numbers. Recurrence numbers were a primary indication for 134/300 patients (45%). After counseling about etiology and risk-reducing strategies, 116 patients (39%) opted to receive recurrence numbers, with most (n = 64, 55%) perceiving the number to be lower than expected. There was no difference in T2 GCOS scores between those who: (1) received recurrence numbers vs those who did not, or (2) perceived the number to be lower than expected vs those with other perceptions. However, a subset of patients who did not receive recurrence numbers had larger increases in GCOS scores. Our data provide impetus to question the assumption that recurrence numbers should be routinely provided in genetic counseling, and show that in naturalistic practice, optimal patient outcomes are not contingent on receipt of recurrence numbers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".