Cancer and fertility: optimizing communication between patients and healthcare providers
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article reviews the status of guidelines and recommendations for communication between patients with cancer and healthcare providers (HCPs) concerning fertility issues. RECENT FINDINGS: The timing, the type of information provided, and the openness of HCPs can all affect how patients with cancer perceive discussions regarding fertility concerns and preservation. In addition, whether such discussions occur is associated with intrinsic factors, such as age and sex of the patients as well as HCP's knowledge level. It has also been found that the patients have different needs for information regarding fertility preservation and preferences for types of communication strategies regarding the impact of their disease and treatments on options for family planning. SUMMARY: Although discussions about fertility concerns in the context of cancer between physicians and patients are occurring more frequently, there are inconsistent findings regarding satisfaction with these discussions. Recent research has found that the timing, type of information given, and level of openness of the HCP can impact how patients perceive communications regarding the risks of cancer treatment on fertility preservation options and future family planning. Age, sex, and HCP's knowledge of fertility risks and fertility preservation services are also notable factors associated with whether and how extensively discussions about fertility take place. More women than men report having a fertility discussion with an HCP. However, men are more likely to report satisfaction with the fertility discussion than women.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".