Abstract B110: Racial disparities in pancreatic adenocarcinoma survival. Do they exist for patients who already survived their first year?
Bibliographic record
Abstract
Abstract Purpose: Population-based studies indicated that prognosis of pancreatic adenocarcinoma (PAC) is worse in black patients compared to other races. Nonetheless, survival probabilities can change over time based on number of years (yr.) already survived by patients; a concept called conditional survival. This study explored the dynamic changes in risk according to patient characteristics, particularly race, on survival of PAC patients using cancer-specific survival (CSS) estimates. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried for data on adult patients with non-metastatic PAC, diagnosed between 1988 and 2010. Patient characteristics, such as age, race, tumor grade, and stage were collected at the time of diagnosis. CSS probabilities, as well as Cox proportional hazard ratios (HRs), were computed at the time of diagnosis (Actuarial CSS and baseline HR), and after already surviving 1 to 6 yr. after diagnosis (Conditional CSS and HR). Harrell’s concordance index (C-index) was used to measure the cross-validation accuracy of the Cox models. Results: Our search retrieved data on 20,491 patients, with a mean age at diagnosis of 67.2 yr. Most of the patients were White (81.6%), followed by Black (12%) and Asians/Pacific Islander (6.4%). The stage was T1-2N0M0 in 15.9%, T3-4N0M0 in 41.8%, and T1-4N1M0 in 42.3% of patients. The 3-yr actuarial CSS calculated from time of diagnosis was significantly different across racial groups, at 11%, 10%, and 13% for Whites, Blacks, and Asians, respectively (P < 0.01). Conversely, for patients who already survived 1 yr. after diagnosis, the probability of surviving an additional 2 yr. was similar across races, at 26.2%, 27.1%, and 29.9%, for Whites, Blacks, and Asians, respectively (P = 0.218). As patients survived for longer periods of time following diagnosis, conditional CSS estimates increased similarly across different races; for White, Black, and Asian patients who already survived 3 yr. after diagnosis, the probability of surviving an additional 2 yr. was 62.6%, 60.5%, and 62.1%, respectively (P = 0.532). In multivariate cox models, the prognostic effect of race lost significance if patients already survived ≥1 yr. after diagnosis (Baseline HR = 1.114, 95%CI [1.045- 1.187], mean C-index = 67%; conditional HR at 1 yr = 1.015, 95%CI [0.919- 1.12], mean C-index = 60%). The prognostic effect of tumor grade, site, and age lost significance if patients already survived ≥2, ≥4, and ≥6 yr. after diagnosis, respectively. Tumor stage maintained its prognostic significance over time (conditional HR at 6 yr = 1.522, 95%CI [1.049- 2.208], mean C-index = 59%). Conclusion: Racial disparities in survival outcomes exist at the time of diagnosis for PAC patients. However, the survival impact of these disparities does not seem to persist over time. Other variables, such as age, tumor grade, stage, and treatment received should be taken into account when predicting future prognosis of PAC patients who have already survived ≥ 1 yr. after diagnosis. Citation Format: Anas M Saad, Maha AT Elsebaie, Mohamed Amgad, Muneer J Al-Husseini, Kyrillus S Shohdy, Omar Abdel-Rahman. Racial disparities in pancreatic adenocarcinoma survival. Do they exist for patients who already survived their first year? [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr B110.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".