Meta-analyses of factors associated with long-term survival after resection of pancreatic ductal adenocarcinoma.
Bibliographic record
Abstract
4165 Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. Long-term survival (>5 years, LTS) is rarely seen even after ‘curative-intent’ resection. With improved systemic control via effective systemic therapies, LTS is now being reported more frequently. However, our understanding of LTS in PDAC remains limited. The aim of the current study was to perform a systematic review and meta-analysis to quantify the associations between various clinicopathological factors and LTS following resection of PDAC. Methods: The PubMed, Embase, Scopus, and Cochrane CENTRAL databases were systematically searched for articles reporting actual patient survival data. Two reviewers independently screened and reviewed articles, extracted relevant data, and assessed the risk of bias in included studies using the Newcastle-Ottawa scale (NOS). Data that compared patients who achieved LTS after resection with those who did not were extracted from the included studies. Meta-analyses using a random effects model were conducted to identify associations between LTS and various patient, tumor, and treatment related factors. Results: Overall, 33 studies with 46,981 patients after resection of PDAC were included. Most articles received a ‘good’ NOS assessment, indicating acceptable risk of bias. The median rate of LTS was 15.27% (IQR: 9.47-20.72). Multiple clinicopathological factors were found to be associated with LTS, including tumor grade (OR: 0.40, 95%CI: 0.31-0.52), tumor stage (OR: 0.36, 95%CI: 0.31-0.41), and margin status (OR: 0.43, 95%CI: 0.36-0.50). Factors that were not associated with LTS included patient age, tumor size, tumor location, or genetic mutations. Notably, adjuvant therapy (OR: 1.68, 95%CI: 1.24-2.28) but not neoadjuvant therapy (OR: 1.08, 95%CI: 0.62-1.87) were associated with LTS. Conclusions: This meta-analysis revealed that 15% of patients achieved LTS after resection of PDAC. Multiple clinicopathological factors are associated with LTS whereas presence of ‘traditional’ negative prognostic factors does not rule out LTS. Further studies are required to identify robust predictors of LTS in resected PDAC. [Table: see text]
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.078 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".