Understanding the Experience of Canadian Women Living with Ovarian Cancer through the Every Woman StudyTM
Bibliographic record
Abstract
The Every Woman StudyTM: Canadian Edition is the most comprehensive study to date exploring patient-reported experiences of ovarian cancer (OC) on a national scale. An online survey conducted in Fall 2020 included individuals diagnosed with OC in Canada, reporting responses from 557 women from 11 Canadian provinces/territories. Median age at diagnosis was 54 (11−80), 61% were diagnosed between 2016−2020, 59% were stage III/IV and all subtypes of OC were represented. Overall, 23% had a family history of OC, 75% had genetic testing and 19% reported having a BRCA1/2 mutation. Most (87%) had symptoms prior to diagnosis. A timely diagnosis of OC (≤3 months from first presentation with symptoms) was predicted by age (>50) or abdominal pain/persistent bloating as the primary symptom. Predictors of an acute diagnosis (<1 month) included region, ER/urgent care doctor as first healthcare provider or stage III/IV disease. Regional differences in genetic testing, treatments and clinical trial participation were also noted. Respondents cited substantial physical, emotional, practical and financial impacts of an OC diagnosis. Our national survey has revealed differences in the pathway to diagnosis and post-diagnostic care among Canadian women with OC, with region, initial healthcare provider, specific symptoms and age playing key roles. We have identified many opportunities to improve both clinical and supportive care of OC patients across the country.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".