“I just wanted more”: Hereditary cancer syndromes patients’ perspectives on the utility of circulating tumour DNA testing for cancer screening
Bibliographic record
Abstract
Abstract Hereditary cancer syndromes (HCS) predispose individuals to a higher risk of developing multiple cancers. However, current screening strategies have limited ability to screen for all cancer risks. Circulating tumour DNA (ctDNA) detects DNA fragments shed by tumour cells in the bloodstream and can potentially detect cancers early. This study aimed to explore patients’ perspectives on ctDNA’s utility to help inform its clinical adoption and implementation. We conducted a qualitative interpretive description study using semi-structured phone interviews. Participants were purposively sampled adult HCS patients recruited from a Canadian HCS research consortium. Thirty HCS patients were interviewed (n=19 women, age range 20s-70s, n=25 were white). Participants were highly concerned about developing cancers, particularly those without reliable screening options for early detection. They “just wanted more” than their current screening strategies. Participants were enthusiastic about ctDNA’s potential to be comprehensive (detect multiple cancers), predictive (detect cancers early) and tailored (lead to the personalized clinical management). Participants also acknowledged ctDNA’s potential limitations, including false positives/negatives risks and experiencing additional anxiety. However, they saw ctDNA’s potential benefits outweighing its limitations. In conclusion, participants’ belief in ctDNA’s potential to improve their care overshadowed its limitations, indicating patients’ support for using ctDNA in HCS care.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".