Real-World Treatment Patterns, Clinical Outcomes, and Healthcare Resource Utilization in Early-Stage Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
The prognosis of early non-small-cell lung cancer (eNSCLC) remains poor. An understanding of current therapies and outcomes can provide insights into how novel therapies can be integrated into clinics. We conducted a large, retrospective, population-based cohort study of patients with de novo eNSCLC (stages IB, IIA, IIB, and IIIA) diagnosed in Alberta, Canada, between 2010 and 2019. The primary objectives were to describe treatment patterns and survival outcomes among patients with eNSCLC. A total of 5126 patients with eNSCLC were included. A total of 45.3% of patients were referred to a medical oncologist, ranging from 23.7% in stage IB to 58.3% in IIIA. A total of 23.6% of patients initiated systemic therapy (ST), ranging from 3.5% in stage IB to 38.5% in IIIA. For stage IIB and IIIA individuals who received surgery, adjuvant ST was associated with a decreased likelihood of death (hazard ratios (HR) of 0.77 (95% CI: 0.56-1.07) and 0.69 (95% CI: 0.54-0.89), respectively). In a Canadian real-world setting, stage IIB and IIIA patients who received adjuvant ST tended to have better survival than patients who did not, but future studies that provide adjustment of additional confounders are warranted. Examining referral pathways that account for disparities based on age, sex, and comorbidities in the real world would also provide further insights.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".