Outcomes of Patients With Advanced Gastrointestinal Cancer in Relationship to Opioid Use: Findings From Eight Clinical Trials
Bibliographic record
Abstract
BACKGROUND: This study assessed the patterns of opioid use among patients with advanced gastrointestinal cancers who were included in 8 clinical trials and evaluated the impact of opioid use on survival outcomes of included patients. METHODS: Deidentified datasets from 8 clinical trials evaluating first-line systemic treatment of advanced gastrointestinal cancers were accessed from the Project Data Sphere platform (ClinicalTrial.gov identifiers: NCT01124786, NCT00844649, NCT00290966, NCT00678535, NCT00699374, NCT00272051, NCT00305188, and NCT00384176). These trials evaluated patients with pancreatic carcinoma, gastric carcinoma, hepatocellular carcinoma (HCC), and colorectal carcinoma. Multivariable logistic regression analysis was used to evaluate factors predicting the use of opioids. Kaplan-Meier survival estimates were used to compare survival outcomes in each disease entity among patients who did or did not receive opioid treatment. Multivariable Cox regression analysis was then used to further assess the impact of opioid use on survival outcomes in each disease entity. RESULTS: A total of 3,441 participants were included in the current analysis. The following factors predicted a higher probability of opioid use within logistic regression analysis: younger age at diagnosis (odds ratio [OR], 0.990; 95% CI, 0.984-0.997; P=.004), nonwhite race (OR for white vs nonwhite, 0.749; 95% CI, 0.600-0.933; P=.010), higher ECOG score (OR for 1 vs 0, 1.751; 95% CI, 1.490-2.058; P<.001), and pancreatic primary site (OR for colorectal vs pancreatic, 0.241; 95% CI, 0.198-0.295; P<.001). Use of opioids was consistently associated with worse overall survival (OS) in Kaplan-Meier survival estimates of each disease entity (P=.008 for pancreatic cancer; P<.001 for gastric cancer, HCC, and colorectal cancer). In multivariable Cox regression analysis, opioid use was associated with worse OS among patients with pancreatic cancer (hazard ratio [HR], 1.245; 95% CI, 1.063-1.459; P=.007), gastric cancer (HR, 1.725; 95% CI, 1.403-2.122; P<.001), HCC (HR, 1.841; 95% CI, 1.480-2.290; P<.001), and colorectal cancer (HR, 1.651; 95% CI, 1.380-1.975; P<.001). CONCLUSIONS: Study findings suggest that opioid use is consistently associated with worse OS among patients with different gastrointestinal cancers. Further studies are needed to understand the underlying mechanisms of this observation and its potential implications.
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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 | 0.033 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".