Psychological impact of genetic testing for hereditary non‐polyposis colorectal cancer
Bibliographic record
Abstract
The psychological impact of predictive genetic testing for hereditary non-polyposis colorectal cancer (HNPCC) was assessed in 114 individuals (32 carriers and 82 non-carriers) attending familial cancer clinics, using mailed self-administered questionnaires prior to, 2 weeks, 4 months and 12 months after carrier status disclosure. Compared to baseline, carriers showed a significant increase in mean scores for intrusive and avoidant thoughts about colorectal cancer 2 weeks (t = 2.49; p = 0.014) and a significant decrease in mean depression scores 2 weeks post-notification of result (t = -3.98; p < 0.001) and 4 months post-notification of result (t = -3.22; p = 0.002). For non-carriers, significant decreases in mean scores for intrusive and avoidant thoughts about colorectal cancer were observed at all follow-up assessment time points relative to baseline. Non-carriers also showed significant decreases from baseline in mean depression scores 2 weeks, 4 months and 12 months post-notification. Significant decreases from baseline for mean state anxiety scores were also observed for non-carriers 2 weeks post-notification (t = -3.99; p < 0.001). These data indicate that predictive genetic testing for HNPCC leads to psychological benefits amongst non-carriers, and no adverse psychological outcomes were observed amongst carriers.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".