Second and later-line erlotinib use in non-small cell lung cancer: real world outcomes and practice patterns overtime in Canada
Bibliographic record
Abstract
BACKGROUND: status. The purpose of this study is to characterise the use of second or later-line erlotinib therapy in Ontario, Canada from 2007-2016, as well as evaluate the impact of erlotinib therapy on survival and emergency department (ED) visits in a real-world population. METHODS: This is a retrospective cohort study derived at ICES (formerly known as the Institute for Clinical and Evaluative Sciences) of advanced NSCLC patients diagnosed from 2007-2016 in Ontario, Canada, over the age of 65, who received at least one dose of first-line chemotherapy. The exposure of interest was receipt of second or later-line erlotinib. The primary outcome was the hazard ratio for mortality evaluated using a Cox proportional hazards model, and the secondary outcome, ED visits, was evaluated using a Poisson model. RESULTS: First-line chemotherapy was administered in 30.4% of stage IV NSCLC patients. Of these patients, 19.7% received second or later-line erlotinib. The proportion of patients prescribed second or later-line erlotinib decreased over the course of the study (P<0.0001). Unadjusted median overall survival in the entire cohort was 325 days (95% CI: 314-337 days), 513 days (95% CI: 485-539 days) in the erlotinib cohort, and 282 days (95% CI: 270-291 days) in the non-erlotinib cohort. Despite this, the adjusted hazard ratio for death was 1.89 (95% CI: 1.73-2.07, P<0.0001) for patients on erlotinib. Patients receiving erlotinib also had a marginally higher relative rates of ED visits with an adjusted relative risk of 1.10 (95% CI: 1.02-1.19, P=0.013). CONCLUSIONS: This study highlights the importance of using EGFR targeted treatments in NSCLC patients with a predictive biomarker, and suggests that treatment with erlotinib therapy is unlikely to benefit unselected patients with advanced NSCLC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".