Impact of Performance Status on Survival Outcomes and Health Care Utilization in Patients With Advanced NSCLC Treated With Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Introduction: Landmark trials testing immune checkpoint inhibitors (ICIs) in advanced NSCLC are difficult to extrapolate to real-world practice given the exclusion of patients with poor (i.e., ≥2) Eastern Cooperative Oncology Group performance status (ECOG PS). We sought to evaluate the impact of ECOG PS on clinical outcomes and health care utilization in patients with NSCLC treated with ICIs in real-world practice. Methods: Patients with advanced NSCLC who received at least one dose of pembrolizumab or nivolumab were retrospectively identified from the Alberta Immunotherapy Database. The primary outcome was median overall survival, as stratified by ECOG PS. Secondary outcomes included median time-to-treatment failure and metrics of health care utilization, including emergency department visits, hospitalizations, and death in hospital. Results: < 0.001). Conclusions: Patients with NSCLC with poor ECOG PS treated with ICI had significantly worse survival outcomes and were significantly more likely to use health care services than those with favorable ECOG PS. The large proportion of patients with poor ECOG PS further justifies the urgent need for randomized trials evaluating the efficacy of ICI in this high-risk population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".