Reflex testing for Lynch syndrome: If we build it, will they come? Lessons learned from the uptake of clinical genetics services by individuals with newly diagnosed colorectal cancer (CRC)
Bibliographic record
Abstract
The aim of this qualitative study was to examine the experience of individuals facing a choice about genetic counselling/testing in the context of newly diagnosed colorectal cancer (CRC). Nineteen individuals with newly diagnosed CRC, including 12 individuals who accepted genetic counselling ("acceptors") and 7 individuals who declined genetic counselling ("refusers"), were interviewed using a standardized questionnaire guide which focused on motivations and barriers experienced in the decision process. Data were analyzed using Karlsson's Empirical Phenomenological method of data analysis (Karlsson in Psychological qualitative research from a phenomenological perspective. Almgvist and Wiksell International, Stockholm, 1993). Three major themes were identified: facing challenges in health literacy; mapping an unknown territory; and adjusting to cancer. The study participants' testimonies provided novel insights into potential reasons for patient non-engagement in pilot studies of reflex testing for Lynch syndrome, and allowed us to formulate several recommendations for enhancing patient engagement. Our study findings suggest that patient engagement in clinical cancer genetics services, including reflex testing for Lynch syndrome, can only be achieved by addressing current health literacy issues, by deconstructing current misconceptions related to potential abuses of genetic information, by emphasizing the clinical utility of genetic assessment, and by adapting genetics practices to the specific context of cancer care.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".