Genetic counselors' preferences for coverage of preimplantation genetic diagnosis: A discrete choice experiment
Bibliographic record
Abstract
Preimplantation genetic diagnosis (PGD) allows couples to test for a genetically affected embryo prior to implantation. Patient access to this ethically complex and expensive technology differs markedly across jurisdictions, with differences in private/public insurance coverage and variations in patient inclusion and diagnostic criteria. The objective of the study was to identify trade-offs regarding PGD coverage decisions amongst genetic counselors. To quantify stated preferences for PGD coverage, we conducted a discrete choice experiment with Canadian genetic counselors (GC) considering attributes regarding the scope of testing (PGD indication, risk of the condition and number of cycles covered) and patient inclusion criteria (fertility status and family history). Multinomial logit regression was used to estimate trade-offs amongst attributes using part-worth utilities and importance scores. The completed response rate was 41% with 126 GC completing the survey. Risk of the genetic condition was the most important attribute. Overall, GC were more responsive to the scope of testing criteria including the condition's risk (importance score of 42%) and PGD indication (31%) rather than family history (11%) and fertility status (8%). Based on this study's attributes and levels, condition characteristics are prioritized even above patient characteristics for PGD coverage.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".