Psychological impact of risk-reducing surgery for gynecologic cancer among women with Lynch syndrome
Bibliographic record
Abstract
• Facilitators to RRS include burden of cancer screening, desire for less cancer worry, and trust in healthcare providers. • Women with LS require more information on what to expect post-RRS regarding physical symptoms. • More education about the risks and benefits of hormone replacement therapy was desired. • Participants had unmet informational needs about impact of RRS on risk of osteoporosis, heart disease and breast cancer. Prophylactic total hysterectomy and bilateral salpingo-oophorectomy are risk-reducing surgeries (RRS) that can be offered to women with Lynch syndrome (LS) as they reduce the incidence of ovarian and endometrial cancer. Few studies have examined facilitators to RRS or the experiences of women with LS post-surgery. This qualitative study explored the experiences of women with LS who underwent RRS. Women with LS who had undergone RRS within the prior 10 years were recruited from a genetic cancer registry and a tertiary care medical centre in Canada. Participants completed interviews over the phone. A qualitative descriptive methodological approach was taken, and interviews were analyzed using thematic analysis. Fifteen participants completed interviews. Themes identified included: 1) facilitators to RRS including desire for peace of mind, completed family planning, presence of physical symptoms associated with gynecological cancer, burden of screening, personal or family history of cancer, age, and trust in healthcare providers (HCPs); 2) women’s experiences with RRS including post-surgical recovery, long-term physical changes post-surgery, impact of surgery on sexual health, psychological impacts of managing risk, and post-surgical care from HCPs; 3) experiences managing menopausal symptoms and use of hormone replacement therapy; and 4) unmet informational needs including managing expectations prior to surgery, understanding risk related to other health conditions, and questions about the ongoing need for gynecological cancer surveillance. HCPs should consider facilitators to surgery in women with LS contemplating RRS. HCPs should also provide women with LS more detailed post-surgery information on what to expect, and risks of other health conditions.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".