Pattern of Treatment Initiation and Outcomes for Patients With Metastatic Non-small Cell Lung Cancer in Ontario
Bibliographic record
Abstract
INTRODUCTION: The impact of diagnosis and treatment delay on outcomes in advanced non-small cell lung carcinoma (NSCLC) is not well understood. In this study, we examined the effect of the length of time to the first chemotherapy treatment initiation and the other factors affecting overall survival. METHODS: This retrospective study used data from the Institute of Clinical Evaluative Sciences and identified 4520 patients in Ontario who were diagnosed with stage IV NSCLC between 2007 and 2016, treated using chemotherapy. We adjusted the analysis for location (rural vs urban), gender, distance from the nearest cancer center, first chemotherapy treatment used, income, and age. Results: Type of the chemotherapy, length of time to the first treatment, and distance from the nearest cancer center had a statistically significant impact on survival. Paclitaxel was associated with decreased risk of death compared to vinorelbine (Hazard Ratio (HR)=0.835, 95%CI 0.753-0.925), gemcitabine (HR=0.916, 95%CI 0.998-0.826), and docetaxel (HR=0.771, 95%CI 0.994-0.513). Every additional 10 km distance from the nearest cancer center was associated with a 0.5% increased risk of death (HR=1.005, 95%CI 1.000-1.010). A longer time to the first treatment was associated with increased survival. In fact, every 10 days increase in wait time was associated with a 0.5% decrease in the risk of death (HR=0.995, 95%CI 0.993-0.998). Conclusion: Chemotherapy treatment using paclitaxel and living closer to the cancer center is associated with better survival. A longer time between diagnosis and treatment leading to better survival could perhaps be explained by patients on the "sicker" end of the spectrum receiving treatment sooner.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".