Age and Comorbidity As Independent Prognostic Factors in the Treatment of Non–Small-Cell Lung Cancer: A Review of National Cancer Institute of Canada Clinical Trials Group Trials
Bibliographic record
Abstract
PURPOSE: This study analyzed patients enrolled in two large, prospectively randomized trials of systemic chemotherapy (adjuvant/palliative setting) for non-small-cell lung Cancer (NSCLC). The main objective was to determine if age and/or the burden of chronic medical conditions (comorbidity) are independent predictors of survival, treatment delivery, and toxicity. PATIENTS AND METHODS: Baseline comorbid conditions were scored using the Charlson comorbidity index (CCI), a validated measure of patient comorbidity that is weighted according to the influence of comorbidity on overall mortality. The CCI score (CCIS) was correlated with demographic data,(ie, age, sex, race), performance status (PS), histology, cancer stage, patient weight, hemoglobin, alkaline phosphatase, lactate dehydrogenase, outcomes of chemotherapy delivery (ie, type, total dose, and dose intensity), survival, and response. RESULTS: A total of 1,255 patients were included in this analysis. The median age was 61 years (range, 34 to 89 years); 34% of patients were elderly (at least 65 years of age); and 31% had comorbid conditions at randomization. Twenty-five percent of patients had a CCIS of 1, whereas 6% had a CCIS of 2 or greater. Elderly patients were more likely to have a CCIS equal to or greater than 1 compared with younger patients (42% v 26%; P < .0001), as were male patients (35% v 21%; P < .0001) and patients with squamous histology (36% v 29%; P = .001). Although age did not influence overall survival, the CCIS appeared prognostic (CCIS 1 v 0; hazard ratio 1.28; 95%CI, 1.09 to 1.5; P = .003). CONCLUSION: In these large, randomized trials, the presence of comorbid conditions (CCIS > or = 1), rather than age more than 65 years, was associated with poorer survival.
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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 | 0.029 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".