Real-world outcomes of patients with advanced gastric cancer treated with FOLFIRI.
Bibliographic record
Abstract
307 Background: Doublet chemotherapy regimens such as 5-fluorouracil and irinotecan (FOLFIRI) appear to be viable first-line treatment options in advanced gastric cancer (AGC). A phase III French intergroup study demonstrated a longer time to treatment failure, improved toxicity profile, and no difference in median OS when using FOLFIRI, compared to epirubicin, cisplatin, and capecitabine (ECX). Purpose: To investigate the real-world efficacy of FOLFIRI in AGC. Methods: Patients diagnosed with AGC in Alberta, Canada from 2012-2020 were identified by the Alberta Cancer Registry. Data points that could not be obtained automatically were obtained by retrospective chart review. Survival was compared by Log-Rank analysis or Cox Regression. Factors associated with FOLFIRI use were determined using logistical regression. Results: A total of 285 patients were analyzed. Eighty-four (29%) patients received FOLFIRI as first-line therapy, 69 received ECX (24%), 82 received CX (29%), 31 received EOX (11%), and 19 received FOLFOX (7%). Thirty-six (12.6%) received a subsequent line of therapy, most commonly FOLFIRI (52.8%). Age, sex, topography (cardia v. non-cardia), or morphology (adenocarcinoma v. signet ring cell v. NOS) did not influence the use of FOLFIRI in the first-line setting. Median overall survival with FOLFIRI was 5.8m vs. 7.4m (p = 0.12) when compared to the patients receiving all other regimens. Conclusions: FOLFIRI had comparable efficacy to other first-line regimens in patients with AGC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".