Oncofertility: a grand collaboration between reproductive medicine and oncology
Bibliographic record
Abstract
In 2007, I was asked by the University of Calgary to participate in a symposium called 'Pushing the Boundaries--Advances that Will Change the World in 20 Years'. My topic was oncofertility, a word I had just coined to describe the intersection of two disciplines--oncology and fertility--and I was thrilled to share my passion for this new field and help young women with cancer protect their future reproductive health. Fertility preservation in the cancer setting lacked a concerted effort to bridge the disciplines in an organized manner. In early 2015, I was delighted to deliver a presentation for the Society for Reproduction and Fertility titled 'Sex in Three Cities', where I gave an update on the oncofertility movement, a remarkable cross-disciplinary, global collaboration created to address the fertility preservation needs of young cancer patients. During my tour of the UK, I was impressed by the interest among the society and its members to engage colleagues outside the discipline as well as the public in a dialogue about cutting-edge reproductive science. In this invited review, I will describe the work of the Oncofertility Consortium to provide fertility preservation options in the cancer setting and accelerate the acceptance of this critical topic on a global scale. I hope that one day this word and field it created will change the world for women who had been left out of the equation for far too long.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".