Assessing disparities in real-world overall survival (rwOS) among sociodemographic groups in advanced ovarian cancer (aOC): The impact of biomarker testing and first-line (1L) maintenance therapies (mtx).
Bibliographic record
Abstract
217 Background: Sociodemographic inequities in aOC survival have been documented and are thought to be largely the result of unequal access to guideline recommended care. In a previous phase of this study with a similar cohort of patients (pts), results showed lower rates of biomarker testing and use of 1L mtx in some socioeconomic status (SES) and racial groups. This study aims to evaluate the association between pts and clinical characteristics on rwOS in aOC. We also estimate how much of the association between race or SES and rwOS is due to biomarker testing and 1L mtx. Methods: An observational cohort study of US pts with newly diagnosed aOC was conducted between 1 Jan 2019 and 30 Dec 2023 using retrospective clinical data from the nationwide Flatiron Health electronic health record-derived deidentified database. Cox regression was used to assess associations between pts and clinical characteristics and rwOS from time of diagnosis, and between biomarker status and receipt of 1L mtx and rwOS. Mediation analysis was used to estimate indirect effects of differences in the probability of receiving recommended biomarker testing and 1L mtx (defined as pts who had known homologous recombination deficiency [HRD] or BRCA status and received a PARP inhibitor [PARPi] if BRCA mutation [BRCAm] or HRD-positive) on rwOS due to race (non-white vs white) and SES (1 [low] vs 2, 3, 4, 5 or unknown). Results: 1287 pts were included in the cohort; median age 68 years (range 22–85); 61% of pts were of white race and 39% non-white; 12% of pts SES 1, 15% SES 2, 21% SES 3, 22% SES 4, 23% SES 5, and 7% SES unknown. Eighty-eight percent of pts received BRCA testing and 49% HRD testing (including BRCAm); 30% (n = 392) received 1L PARPi mtx. Younger age, FIGO stage III, Eastern Cooperative Oncology Group 0, serous histology, and receipt of surgery were all associated with improved rwOS. Receipt of biomarker testing and 1L mtx also had a strong association with improved rwOS. In the mediation analysis, the indirect effect of race through receipt of biomarker testing and 1L mtx was statistically significant and accounted for most of the difference in rwOS between white and non-white pts (Table). Conclusions: Lower rates of BRCA and HRD testing and 1L mtx were associated with worse rwOS. Much of the association in disparities in rwOS by race or SES was due to differences in modifiable risk factors related to biomarker testing and treatment patterns. This suggests that mitigating these differences has the potential to improve outcomes in non-white and lower SES pts with aOC. Effect of race or SES on median rwOS (95% CI), months. Indirect effect via BT + 1L mtx Direct effect other than BT + 1L mtx Total Race non-white −1.7 (−3.6, −0.1) −0.8 (−8.1, 6.6) −2.5 (−9.6, 5.0) SES 1 (low) −1.5 (−3.6, 0.4) −2.0 (−11.3, 8.7) −3.4 (−12.8, 7.3) BT, biomarker testing.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".