Variations in Ovarian Cancer Survival Rates: Investigating Equity and Prognostic Factors Throughout Nova Scotia
Bibliographic record
Abstract
IntroductionThere is large inter- and intra-country variability in ovarian cancer outcomes. Individuals diagnosed with advanced stage cancer in Nova Scotia have a 3-year net survival of 31.9%, the lowest in the country. This study aimed to identify factors impacting survival, and to investigate evidence of inequities in survival from the point of diagnosis moving forward.MethodsThis population-based retrospective study included all women diagnosed with ovarian cancer in Nova Scotia from Jan 1, 2007, to Dec 31, 2016. Administrative health data were linked to gather individual, tumor, and health system characteristics. Both prognostic and equity factors potentially contributing to variations and inequities in survival were assessed using descriptive and time to event techniques.ResultsThis study found no regional differences in survival across Nova Scotia. It revealed that disparities in equity factors do not appear to be significantly associated with survival at the time of diagnosis moving forward. Instead, survival variations were attributed to legitimate prognostic factors, such as cancer stage, subtype, comorbidities, and frailty. However, notable inequities were identified between socioeconomic status and prognostic factors that may contribute to poor survival upstream, rather than at the time of diagnosis.ConclusionThough inequities do not appear to directly contribute to differences in ovarian cancer survival at the time of diagnosis, they may influence outcomes by increasing the development of prognostic factors that lead to poorer survival. Future research should capture equity factors not found in administrative data and begin making comparisons between other jurisdictions to determine why survival rates vary worldwide.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".