Awareness and practices of Arab oncologists towards oncofertility in young women with cancer
Bibliographic record
Abstract
Background: Cancer in young women is a major health problem in the Middle Eastern and North African population. We explored the awareness, barriers and practice of Arab oncologists towards oncofertility. Methods: Oncologists from Arab countries treating female cancer patients were invited to complete a 30-item web-based questionnaire that explores oncologists' demographics, available techniques and barriers to oncofertility. Results: 170 oncologists working in 9 different Arab countries responded to the questionnaire. Among the responders, 89 (52.4%) were from Egypt and the central region, 60 (35.3%) were from North Africa and 21 (12.4%) were from the Gulf region.While most participants considered a dedicated training 'necessary', only 43 oncologists (25.3%) received a formal training. Only 17 participants (10%) had a fertility clinic in their centre, 44 (25.9%) and 13 (7.6%) had to refer patients to other centres or other cities, respectively. A total of 96 oncologists (56.5%) did not have access to a fertility preservation service.Out of 147 responders, 79 (53.7%) offered fertility preservation only in patients presenting with early disease and 38 (25.9%) did not offer fertility preservation. In terms of proposed strategies, 50 responders (29.4%) offered embryo cryopreservation, 79 (46.5%) oocyte cryopreservation and 48 (28.2%) ovarian tissue cryopreservation. Conclusion: A large gap exists between international clinical practice guidelines and current practices of fertility preservation in Arab countries. Barriers to optimum service delivery include the lack of physician awareness/training, unavailability of some advanced techniques and a lack of dedicated fertility clinics within the cancer centres.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".