Factors Associated With Adolescent and Young Adult Females Attending a Fertility Consultation Within 30 Days of a Cancer Diagnosis in Ontario, Canada
Bibliographic record
Abstract
Purpose: To examine the factors associated with females attending a fertility consultation within 30 days of cancer diagnosis. Methods: This is a retrospective cohort study, including females, 15 to 39 years of age, diagnosed with cancer in Ontario, Canada. Administrative data were used from the Institute of Clinical and Evaluative Sciences for the period 2006 to 2019. A backward selection multivariate logistic regression was performed, with a primary outcome of fertility consultation within 30 days of diagnosis. Results: A total of 20,556 females were included in the study, with 7% having attended a fertility visit within 30 days of diagnosis. Factors associated with being more likely to attend included: not currently having children (odds ratio [OR] = 4.3; confidence interval [95% CI 3.6–5.1]), later years of diagnosis (OR = 3.2; 95% CI [2.8–3.8]), having undergone chemotherapy (OR = 3.6; 95% CI [3.0–4.3]) or radiation therapy (OR = 1.9; 95% CI [1.6–2.2]), and less marginalization within dependency quintiles (OR 1.4; 95% CI [1.1–1.7]). Having a cancer with lower risk to fertility (OR = 0.3; 95% CI [0.2–0.3]), death within a year of diagnosis (OR = 0.4; 95% CI [0.3–0.6]), and residing in a northern region of Ontario (OR = 0.3; 95% CI [0.2–0.4]) were associated with being less likely to attend. For sociodemographic factors, lower levels of income (OR = 0.5; 95% CI [0.4–0.6]) and marginalization with residential instability (OR = 0.6; 95% CI [0.5–0.8]) were associated with being less likely to attend a fertility consultation. Conclusions: Rates for attendance of female fertility consultations after a cancer diagnosis remain low, with disparities by both clinical and demographic factors.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".