Knowledge, Practice, and Attitudes of Physicians in Low- and Middle-Income Countries on Fertility and Pregnancy-Related Issues in Young Women With Breast Cancer
Bibliographic record
Abstract
PURPOSE: Fertility and pregnancy-related issues are highly relevant for young (≤ 40 years) patients with breast cancer. Limited evidence exists on knowledge, practice, and attitudes of physicians from low- and middle-income countries (LMICs) regarding these issues. METHODS: A 19-item questionnaire adapted from an international survey exploring issues about fertility preservation and pregnancy after breast cancer was sent by e-mail between November 2019 and January 2020 to physicians from LMICs involved in breast cancer care. Descriptive analyses were performed. RESULTS: A total of 288 physicians from Asia, Africa, America, and Europe completed the survey. Median age was 38 years. Responders were mainly medical oncologists (44.4%) working in an academic setting (46.9%). Among responders, 40.2% and 53.8% reported having never consulted the available international guidelines on fertility preservation and pregnancy after breast cancer, respectively. 25.0%, 19.1%, and 24.3% of responders answered to be not at all knowledgeable about embryo, oocyte, or ovarian tissue cryopreservation, respectively; 29.2%, 23.6%, and 31.3% declared that embryo, oocyte, and ovarian tissue cryopreservation were not available in their countries, respectively. 57.6% of responders disagreed or were neutral on the statement that controlled ovarian stimulation can be considered safe in patients with breast cancer. 49.7% and 58.6% of responders agreed or were neutral on the statement that pregnancy in breast cancer survivors may increase the risk of recurrence overall or only in those with hormone receptor-positive disease, respectively. CONCLUSION: This survey showed suboptimal knowledge, practice, and attitudes of physicians from LMICs on fertility preservation and pregnancy after treatment completion in young women with breast cancer. Increasing awareness and education on these aspects are needed to improve adherence to available guidelines and to promote patients' oncofertility counseling.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".