Pancreatic cancer outcomes in Nova Scotia: Searching for small windows of opportunity.
Bibliographic record
Abstract
e16219 Background: While pancreatic cancer (PC) globally has poor outcomes, there are still regional variation in PC outcomes in Canada. Nova Scotia (NS) has been documented to have some of the worst outcomes in PC but the details and barriers to the care of PC in NS have never been analyzed. Methods: A retrospective chart review was performed on PC patients (pts) diagnosed in NS from 2013 to 2017 for patient characteristics, referral pattern, treatments and wait times. Cox proportional hazards methods were used to analyze overall survival (OS) with Age, Stage, Eastern Cooperative Oncology Group Performance Status (PS), Charleston Comorbidity Index (CCI), receiving ERCP and receiving chemotherapy as covariates in the multivariate analysis. Results: 667 consecutive pts were identified, which included 357 males and 310 females with a median age of 71 at diagnosis. 42 (6.25%) lived beyond 2 years, while 163 (24.4%) survived for under 30 days and 260 (39%) survived for under 60 days. Patients with a limited survival (under 30 days) when compared to pts who survived > 60 days are older (mean 75 vs 71, P < 0.05), had a higher proportion of ECOG > 2 (81.6% vs 20.3%, P < 0.01), and a higher proportion of stage 4 disease (73.9% vs 41.2%, P < 0.01). There was no significant difference in any measure of wait times. Pts with limited survival were less likely to be seen by Medical Oncology (MO) (20.9% vs 70.9%, P < 0.001), and less likely to receive chemotherapy (1.2% vs 45%, P < 0.001) or ERCP (27% vs 53.8%, P < 0.01). Multivariate analysis showed that receiving ERCP (P = 0.027) and chemotherapy(P < 0.001) are independent predictors of survival, even when accounting for PS, CCI, stage, and age. Conclusions: Analysis of PC outcomes in NS demonstrates a large proportion of pts dying within 30 days of diagnosis. Those pts are older and present with higher stage and worse PS but did not have any significant difference in diagnostic and referral wait times. Those pts receive fewer referrals to Oncology services, fewer potentially life prolonging treatments and we uniquely discovered ERCP as an independent predictor of survival in our population. While further work is needed, this study characterized some of the unique challenges of PC care in NS as a province with a higher proportional of older adults and highlights potential opportunities to improve early healthcare delivery in older adults with limited windows for care.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".