Fertility‐Related Concerns in Survivors of Childhood Cancer: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Childhood cancer survivors are at increased risk for treatment-related fertility impairment and subsequent fertility-related concerns. However, there is currently no consensus on how to define, assess, and address these concerns in clinical practice. Therefore, we aimed to answer the following questions: (1) How are fertility-related concerns assessed and defined? (2) What types of fertility-related concerns are reported by survivors, and how common are they? (3) What clinical implications are proposed in the literature? METHODS: We systematically searched the databases PubMed, PsycINFO, MEDLINE, CINAHL, and Embase for peer-reviewed publications on fertility-related concerns in survivors of childhood cancer published between January 1990 and November 2023. The search yielded 2077 potentially relevant articles, of which eleven met inclusion criteria and were included. Furthermore, we screened the reference lists of included studies, leading to a total of N = 25 studies included in the narrative synthesis. RESULTS: We identified considerable variation in the assessment, description, and definition of fertility-related concerns. Survivors described fertility-related concerns in terms of their personal and reproductive health, the health of their (future) children, and dating and romantic relationships. Concerns were reported by 9.5% to 65% of survivors in quantitative studies. Suggestions for clinical implications included that different healthcare providers could use individual or group strategies to address fertility-related concerns. They should focus on patient reports rather than parent-proxy reports and assess individual fertility-related preferences and attitudes without making assumptions. CONCLUSIONS: Fertility-related concerns are common among childhood cancer survivors and need to be addressed by care providers and researchers in the field. To do so effectively, we need more consistency in assessing, labeling, and defining these concerns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".