Phase II Multicenter Study of Induction Chemotherapy Followed by Concurrent Efaproxiral (RSR13) and Thoracic Radiotherapy for Patients With Locally Advanced Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Efaproxiral (RSR13) reduces hemoglobin oxygen-binding affinity, facilitates oxygen release, and increases tissue pO2. We conducted a phase II multicenter study that assessed the efficacy and safety of efaproxiral when administered with thoracic radiation therapy (TRT), following induction chemotherapy, for treatment of locally advanced non-small-cell lung cancer (NSCLC). PATIENTS AND METHODS: Fifty-one patients with locally advanced NSCLC were enrolled at 13 sites. Treatment comprised two cycles of paclitaxel (225 mg/m2) and carboplatin (area under the curve, 6), 3 weeks apart, followed by TRT (64 Gy/32 fractions) with concurrent efaproxiral (50 to 100 mg/kg). Survival results were compared with results of study Radiation Therapy Oncology Group (RTOG) 94-10. RESULTS: Overall response rate was 75% (37 of 49 patients). Complete and partial response rates were 6% (three of 49 patients) and 69% (34 of 49 patients), respectively. Median survival time (MST) was 20.6 months (95% CI, 14.0 to 24.2); overall survival rates at 1- and 2-years were 67% and 37%, respectively. Survival results were compared with the sequential (S-CRT) and concurrent (C-CRT) chemoradiotherapy arms of RTOG 94-10. MSTs for cases matched by stage, Karnofsky performance status, and age were: RT-010, 20.6 months; S-CRT, 15.1 months; and C-CRT, 17.9 months. Grade 3 to 4 toxicities related to efaproxiral that occurred in more than one patient included transient hypoxemia (19%), radiation pneumonitis (11%), and fatigue (4%). CONCLUSION: Addition of efaproxiral to S-CRT represents a promising approach in NSCLC treatment, and a randomized study should be pursued. The low incidence of grade 3 to 4 toxicities suggests that the use of efaproxiral instead of a cytotoxic agent, as a radiation sensitizer, may be advantageous.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".