Development and Validation of an Indicator for Oncofertility Care in Ontario, Canada, for Adolescents and Young Adults with Cancer
Bibliographic record
Abstract
Introduction: There is a lack of metrics to monitor and evaluate fertility care in adolescents and young adults with cancer. This study evaluated the indicator “proportion of cases attending a fertility consult visit ≤30 days from diagnosis of cancer” using the National Quality Forum (NQF) criteria. Methods: This was a retrospective cohort study using administrative data available through the Institute of Clinical Evaluative Sciences in Ontario, Canada. Cases were included if they were diagnosed with a cancer between January 2005 and December 2019, and aged 15–39 years. Fertility consultations were identified by Ontario Health Insurance Plan Claims Database (OHIP) diagnostic codes 628 and 606.Validity was assessed by examining expected differences in the proportion of fertility consults within clinical and demographic factors using chi-square tests. Reliability was assessed by comparing fertility consult visits identified using OHIP diagnostic codes with consults identified using visits to physicians in a registered specialty, using Pearson's correlation coefficient. Results: The population was composed of 39,977 cases, with 6524 (16.3%) having attended a fertility consult. For diagnostic years 2016–2019, differences in the proportion of cases receiving their first fertility consult within 30 days of diagnosis were observed for sex, age, cancer type, hospital type, Local Health Integration Unit, and region ( p < 0.001). There was no correlation between the time from diagnosis to fertility consult and time from diagnosis to the first visit to a fertility-related specialty ( r = 0.11; p = 0.002). Conclusion: The indicator examined in this article adhered to the criteria described by the NQF, providing a possible metric for reporting on oncofertility care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".