Prognostic factors among older adults with advanced non-small cell lung cancer (NSCLC): A multisite cohort study.
Bibliographic record
Abstract
11540 Background: Prognosis in NSCLC is vital for clinical decision making. With the aging US population and rapidly changing treatment landscape, we aimed to identify prognostic factors among older adults with advanced NSCLC receiving chemo-, immuno-, and/or targeted therapy. Methods: We conducted a prospective cohort study of adults age ≥65 with advanced NSCLC starting a new non-curative systemic treatment (chemo-, immuno-, and/or targeted therapy) at a Comprehensive Cancer Center, Veterans Affairs Medical Center, and safety-net hospital. Prior to treatment initiation, patients completed a geriatric assessment including cognition, function, comorbidities, mood, social support, and quality of life. Cox proportional hazards models were performed to identify prognostic factors for overall survival (OS). Results: In a sample of 51 patients, median age was 73 (range 65-94). The majority of patients had stage IVB (59%) or IVA (39%) NSCLC. Current treatment included immunotherapy (37%), targeted therapy (29%), chemoimmunotherapy (18%), and chemo (16%). Most patients had received prior NSCLC treatment (80%): chemo (51%), targeted therapy (35%), immunotherapy (22%), radiation (RT; 47%), and/or surgery (19%). At enrollment, 73% had an abnormal Montreal Cognitive Assessment score < 26 (MoCA; median score 23) and 35% had an abnormal Timed Up and Go time ≥13.5 secs (TUG; median time 12.7 secs). Median OS was 12.5 months. In univariable analyses, stage IVB disease (HR 6.99, 95% CI 1.55-31.5), prior RT (HR 2.98, 95% CI 1.08-8.21), worse MoCA score (HR 1.15 per 1 point change, 95% CI 1.03-1.28), and longer TUG time (HR 1.13 per 1 sec change, 95% CI 1.05-1.23) were associated with worse OS. Of note, age, current NSCLC treatment, line of therapy, and Karnofsky Performance Status were not associated with OS. In multivariable analysis, MoCA score was the only statistically significant prognostic factor (HR 1.15, 95% CI 1.01-1.30). Conclusions: We found that abnormal pretreatment cognition is very common and an important prognostic factor among older adults with advanced NSCLC. Pretreatment screening for cognitive impairment should be considered to inform prognostication, decision making, and treatment planning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".