The Lived Experience of Seeking Pregnancy in a Woman with a History of Cancer
Bibliographic record
Abstract
Myelodysplastic syndromes (MDS) are a rare form of cancer that affects the bone marrow's ability to produce mature blood cells. Several treatments are available, including chemotherapy using anticancer or cytotoxic drugs, blood or platelet transfusions, and stem cell transplants. In this study, a 33-year-old woman shares her experience of attempting to conceive while undergoing treatment for myelodysplastic syndromes. The study aimed to explore the psychosocial difficulties women with a history of cancer treatment may encounter when trying to get pregnant. Semi-structured qualitative interviews were undertaken, recorded, and transcribed verbatim. The transcript was analyzed by narrative analysis. Four themes were identified: 1) Support from loved ones, 2) Challenges in conceiving, 3) Emotional ups and downs during pregnancy, and 4) The joy of motherhood. A cancer diagnosis can devastate young women, primarily if the treatment affects their fertility. These women must have the support of their husbands. Fortunately, many methods are available to assist women in successfully conceiving, although the journey can be difficult and emotional. The ups and downs of the process are inevitable, but the desire to become a mother makes it all worth it in the end. Cancer treatment for myelodysplastic syndromes and related conditions can profoundly impact childbearing women. Such women may face significant challenges if they plan to have a child after treatment. Hence, further research with more women with the diagnosis of MDS is imperative in this critical area.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".