Premature ovarian insufficiency in female adolescent and young adult survivors of non-gynecological cancers: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: The risk of premature ovarian insufficiency (POI) is increased in adolescent and young adult (AYA) cancer survivors, with the prevalence depending on cancer diagnosis, treatment, and patient factors. Prior studies are limited by sample size and type of cancer included. The objective of this study was to assess the risk of POI in female AYA survivors of non-gynecologic cancers, using a population-based approach. METHODS: This population-based retrospective cohort study comprises 21,666 females, 15-39 years old, diagnosed with a single non-gynecologic cancer in Ontario, Canada from 1995 to 2015. Through health administrative data linkage, participants were followed until their 40th birthday, December 31, 2018, bilateral oophorectomy, loss of health insurance eligibility or death. Each cancer survivor was matched to 5 females who were not diagnosed with cancer (unexposed, n = 108,330). Women with bilateral oophorectomy or a prior menopause diagnosis were excluded. POI was identified through use of the ICD-9 code for menopause (ICD9-627). Modified Poisson regression models were used to calculate the adjusted relative risk (aRR) of POI for AYA cancer survivors compared to unexposed individuals, adjusted for income, parity, age, and immigration status. RESULTS: The occurrence of POI was higher in survivors of AYA cancer versus unexposed patients (5.4% vs. 2.2%). Survivors of AYA cancer had an increased risk of POI relative to unexposed patients (aRR 2.49; 95% CI 2.32-2.67). Risk varied by type of cancer: breast (4.32; 3.84-4.86), non-Hodgkin's lymphoma (3.77; 2.88-4.94), Hodgkin's lymphoma (2.37; 1.91-2.96), leukemia (14.64; 10.50-20.42), thyroid (1.26; 1.09-1.46) and melanoma (1.04; 0.82-1.32). Risk varied by age at time of cancer diagnosis, with a higher risk among females diagnosed at age 30-39 years (3.07; 2.80-3.35) than aged 15-29 years (1.75; 1.55-1.98). CONCLUSIONS: AYA survivors of non-gynecologic cancers are at an increased risk of POI, particularly survivors of lymphomas, leukemia, breast, and thyroid cancer. The risk of POI is increased for those diagnosed with cancer at an older age. These results should inform reproductive counseling of female AYAs diagnosed with cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".