Real-world treatment patterns, healthcare resource use and clinical outcomes of patients receiving second line therapy for advanced or metastatic gastric cancer
Bibliographic record
Abstract
Abstract Background Second-line (2 L) chemotherapies for advanced or metastatic gastric cancer have shown improved survival but there is no commonly accepted standard of care. This study examines real-world patient characteristics, treatment patterns, healthcare resource use (HCRU) and clinical outcomes in this setting. Methods Retrospective chart reviews were performed at participating institutions from Australia, Canada, Italy and UK for adult patients receiving 2 L treatment for advanced/metastatic disease from January 2013 to July 2015. Data were collected for 12 months or until death. Results Two hundred eighty patients were included, mean age was 60.9 years and 68.9% were male. Half (51.8%) received monotherapy in 2 L, of whom 69.0% received taxanes. Irinotecan monotherapy was common in Australia (30.0% of monotherapy patients) and Canada (43.8%), but infrequent in Italy and UK. Doublet chemotherapy was used in 36.4% of 2 L patients, most commonly fluoropyrimidine + irinotecan. Use of targeted therapies (trastuzumab, ramucirumab) was infrequent except in Italy. Estimated median real-world progression-free survival (rwPFS) and real-world overall survival (rwOS) from the time of 2 L treatment initiation was 3.09 (95% CI: 2.76–3.68) and 6.54 (5.29–7.76) months, respectively, and estimated 12-month rwPFS and rwOS rate was 8 and 26%, respectively. Only a minority (26.8%) of patients were hospitalized during the follow-up period, with the lowest hospitalization in Italy (16.7%). Laboratory and imaging tests were performed for 93.2 and 70.4%, respectively. Conclusions About half of patients received monotherapy as 2 L chemotherapy for advanced/metastatic gastric cancer and a third received doublets. Real-world clinical outcomes for 2 L treatment are poor and HCRU is considerable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".