Time-to-Treatment Initiation and Its Effect on All-Cause Mortality: Insights From the Surveillance, Epidemiology, and End Results Database
Bibliographic record
Abstract
Background: Delays in cancer treatment initiation can significantly impact survival outcomes, but the magnitude of this effect varies by cancer type, stage, and patient demographics. This study examined the association between time-to-treatment initiation (TTI) and all-cause mortality across multiple common cancers, evaluating differential impacts and sociodemographic disparities. Methods: A retrospective cohort analysis was conducted using the Surveillance, Epidemiology, and End Results (SEER) database, including 991,771 adults diagnosed with breast, lung, prostate, or colorectal cancers between 2015 and 2020. TTI intervals were divided into four categories: 0 - 1, 2 - 5, 6 - 9, and ≥ 10 months. Cox proportional hazards models, adjusted for demographic, socioeconomic, cancer-specific, and treatment factors, assessed the impact of TTI on all-cause mortality, accounting for time-varying covariates. Results: Overall, 63.9% of patients initiated treatment within 1 month. Unadjusted analyses revealed paradoxically lower mortality with longer TTI intervals (26.1% for 0 - 1 month vs. 11.4% for ≥ 10 months). After adjusting for time-varying effects, longer TTI significantly correlated with higher mortality risks (hazard ratio (HR): 1.02 for 2 - 5 months, 1.08 for 6 - 9 months, 1.23 for ≥ 10 months; P < 0.001 each), compared to treatment within 1 month. Older age (HR: 1.06), male gender (HR: 1.08), unmarried status (HR: 1.06), and non-Hispanic Black race (HR: 1.03) were independently associated with increased mortality. Lung cancer patients had significantly higher mortality than breast, prostate, and colorectal cancers (all P < 0.001). Treatment differences emerged, with reduced chemotherapy (40.2% to 10.0%) and surgical interventions (70.6% to 48.8%) at longer intervals. Conclusion: Our analysis showed that increased TTI is independently associated with significantly higher all-cause mortality across major cancers, emphasizing the urgency of timely treatment initiation. Sociodemographic disparities in TTI and outcomes highlight systemic barriers disproportionately affecting vulnerable populations, necessitating targeted interventions to improve equitable cancer care and survival outcomes.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".