Real-world treatment attrition rates in advanced esophagogastric cancer.
Bibliographic record
Abstract
317 Background: Over the last decade, multiple agents have demonstrated efficacy for advanced esophagogastric cancer (EGC), including ramucirumab, irinotecan, trifluridine/tipiracil, and immunotherapy. Despite the availability of later lines of therapy, there remains limited real-world data about the treatment attrition rates between lines of therapy. We sought to characterize the use and attrition rates between lines of therapy for patients with advanced EGC. Methods: We identified patients who received at least one cycle of chemotherapy for advanced EGC between July 1, 2017 and July 31, 2018 across 6 regional centers in British Columbia (BC), Canada. Clinicopathologic, treatment, and outcomes data were extracted by chart review. Results: Of 169 patients who received at least one line of therapy, median age was 65.2 years (IQR 58-72) and 128 (76%) were male, ECOG PS 0/1 (84%), gastric vs GEJ (35% vs 65%). Histologies included adenocarcinoma (76%), squamous cell carcinoma (10%) and signet ring (14%), with 26% HER2 positive. 62% presented with de novo disease, and 35% had received previous chemoradiation. There was a high level of treatment attrition, with patients receiving only one line of therapy (n = 73, 43%), two lines (n = 65, 38%), three lines (n = 25, 15%), and four lines (n = 6, 4%). Kaplan-Meier survival analysis demonstrated improved survival with increasing lines of therapy (median overall survival 9.6 vs. 18.5 vs. 25.8 vs. 40.7 months, p< 0.05). On multivariable Cox regression, improved survival was associated with better baseline ECOG, longer duration of first-line therapy, and increased lines of therapy ( p< 0.01). Conclusions: The steep attrition rates between therapies highlight the unmet need for more efficacious earlier-line treatment options for patients with advanced EGC. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".