Chemotherapy Alone<i>vs.</i>Chemotherapy Plus High Dose Multiple Antioxidants in Patients with Advanced Non Small Cell Lung Cancer
Bibliographic record
Abstract
OBJECTIVE: In vitro and animal studies suggest that antitumor effect of chemotherapeutic agents may be enhanced by antioxidants. Therefore, we initiated a clinical study to test the efficacy of high-dose multiple antioxidants (vitamins C, E and beta carotene) as an adjunct to chemotherapy (paclitaxel and carboplatin) in non-small-cell lung cancer. METHODS: 136 patients of stage IIIb and stage IV NSCLC were randomized to receive chemotherapy (paclitaxel and carboplatin) alone (chemotherapy arm, n = 72) or chemotherapy in combination with ascorbic acid 6100 mg/day, dl-alpha-tocopherol (vitamin E) 1050 mg/day and beta-carotene 60 mg/day (combination arm, n = 64). Survival were calculated by the Kaplan-Meier method and compared using the log-rank test. RESULTS: An overall response rate (RR) of 33% was observed in chemotherapy arm with 24 patients showing a partial response (PR) and none showing a complete response (CR). In combination arm the overall RR was 37% with 24 patients showing PR and two showing CR. The median survival times in chemotherapy arm and combination arm were nine and 11 months respectively. The overall survival (OS) rates in chemotherapy arm and combination arm at one year were 32.9% and 39.1%, and at two years, 11.1% and 15.6% respectively. None of these differences were statistically significant (p = 0.20). Toxicity profiles were similar in both arms. CONCLUSIONS: These results do not support the concern that antioxidants might protect cancer cells from the free radical damage induced by chemotherapy. Larger trials are needed to demonstrate whether high-dose multiple antioxidants in conjunction with chemotherapy increase the response rates and/or survival time in advanced lung cancer.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".