Targeting the Epidermal Growth Factor Receptor in Addition to Chemotherapy in Patients with Advanced Pancreatic Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Overexpression of epidermal growth factor receptors (EGFR) occurs in >90% of pancreatic ductal adenocarcinomas (PDACs) and is associated with a poorer prognosis. A systematic review of electronic databases identified studies exploring the addition of EGFR-targeted treatment to chemotherapy in patients with locally advanced (LA)/metastatic PDAC. Efficacy, safety and tolerability of EGFR-targeted therapy were explored using meta-analysis of randomised controlled trials (RCTs). Meta-regression was utilised to explore factors associated with improved prognosis (all studies) and benefit from EGFR-targeted therapy (RCTs). Twenty-eight studies (7 RCTs and 21 cohort studies) comprising 3718 patients were included. The addition of EGFR-targeted treatment to chemotherapy did not improve progression-free (pooled hazard ratio (HR): 0.90, p = 0.15) or overall survival (HR: 0.94, p = 0.18). EGFR-targeted therapy was associated with increased treatment-related deaths (pooled odds ratio (OR): 5.18, p = 0.007), and grade (G)3/4 rash (OR: 4.82, p = 0.03). There was a borderline significant increase in G3/4 diarrhoea (OR: 1.75, p = 0.06), but no effect on treatment discontinuation without progression (OR: 0.87, p = 0.25). Neither G3/4 rash nor diarrhoea were associated with increased survival benefit from EGFR-targeted therapy. The effect of EGFR-targeted therapy on overall survival (OS) appeared greater in studies with a greater proportion of LA rather than metastatic patients (R = −0.69, p < 0.001). Further studies in unselected patients with advanced PDAC are not warranted. The benefit from EGFR inhibitors may be limited to patient subgroups not yet clearly defined.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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, 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".