Cisplatin and Etoposide Versus Carboplatin and Paclitaxel With Concurrent Radiation for Stage III Non–Small-Cell Lung Cancer: Is There an Impact on Radiation Pneumonitis Rates?
Bibliographic record
Abstract
The article by Santana-Davila et al 1 compared etoposide and cisplatin (EP) with carboplatin and paclitaxel (CP) when delivered as part of concurrent chemoradiotherapy (CCRT) for stage III non-small-cell lung cancer (NSCLC).By using the Veterans' Affairs (VA) Central Cancer Registry, the authors conducted a robust comparison of overall survival (OS) with EP versus CP, using a Cox proportional hazards model, propensity score techniques, and instrumental variable analysis.OS outcomes were not different between the two regimens, but EP was associated with higher rates of complications, including hospitalization, infection, acute renal disease/dehydration, and mucositis/esophagitis.The authors correctly point out that the findings are in contradistinction to a phase II randomized controlled trial 2 (RCT) that had suggested superior OS with EP compared with CP.A potential concern with the use of CP as part of CCRT for stage III NSCLC has been the risk of radiation pneumonitis (RP).RP can adversely affect quality of life, and can uncommonly lead to long-term oxygen dependence or death.Taxanes are potent radiosensitizers, and an individual patient data meta-analysis identified CP use as a major risk factor for RP, with an odds ratio of 5.52 of developing symptomatic RP with CP compared with EP. 3 The risk was highest in elderly patients (age 65 years) who received CP; such patients had an RP risk greater than 50%.Similarly, in the RCT noted above, the RP rate was 48.5% in the CP arm and 25% in the EP arm (P .09). 2 In their study, Santana-Davila et al 1 used International Classification of Diseases, 9th revision, codes to identify toxicities within the VA database but did not include RP (International Classification of Diseases, 9th revision, code 508.0) as one of the outcomes assessed.A comparison of RP rates with CP versus EP within the VA database would help to address this concern regarding pulmonary toxicity.The pathogenesis and risk of RP may differ depending on ethnicity, 4 analogous to the increased risk of hematologic toxicity demonstrated in Asian versus non-Asian patients receiving CCRT for NSCLC. 5The meta-analysis and RCT contained many Asian patients, whereas the VA database contains mostly white male patients 1 ; therefore, the high risk of RP associated with CP in the two former studies may not generalize to the VA population.Could the authors provide additional data analysis comparing RP rates between the CP and EP groups in their study?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".