Variations in Ovarian Cancer Survival Rates: Investigating Equity and Prognostic Factors Throughout Nova Scotia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it