Improvements in Clinical Trials Information Will Improve the Reproductive Health and Fertility of Cancer Patients
Bibliographic record
Abstract
There are a number of barriers that result in cancer patients not being referred for oncofertility care, which include knowledge about reproductive risks of antineoplastic agents. Without this information, clinicians do not always make recommendations for oncofertility care. The objective of this study was to describe the level of reproductive information and recommendations that clinicians have available in clinical trial protocols regarding oncofertility management and follow-up, and the information that patients may receive in clinical trials patient information sheets or consent forms. A literature review of the 71 antineoplastic drugs included in the 68 clinical trial protocols showed that 68% of the antineoplastic drugs had gonadotoxic animal data, 32% had gonadotoxic human data, 83% had teratogenic animal data, and 32% had teratogenic human data. When the clinical trial protocols were reviewed, only 22% of the protocols reported the teratogenic risks and 32% of the protocols reported the gonadotoxic risk. Only 56% of phase 3 protocols had gonadotoxic information and 13% of phase 3 protocols had teratogenic information. Nine percent of the protocols provided fertility preservation recommendations and 4% provided reproductive information in the follow-up and survivorship period. Twenty-six percent had a section in the clinical trials protocol, which identified oncofertility information easily. When gonadotoxic and teratogenic effects of treatment were known, they were not consistently included in the clinical trial protocols and the lack of data for new drugs was not reported. Very few protocols gave recommendations for oncofertility management and follow-up following the completion of cancer treatment. The research team proposes a number of recommendations that should be required for clinicians and pharmaceutical companies developing new trials.
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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 | 0.334 | 0.559 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".