Long-Term Remission After Gefitinib Therapy in an Elderly Patient With Advanced Non-Small-Cell Lung Carcinoma
Bibliographic record
Abstract
The prognosis for non-small-cell lung carcinoma (NSCLC) pat ients in advanced stages is poor. Gefitinib inhibits the tyrosine kinase activity of epidermal growth factor receptor (EGFR) and have been studied extensively. Oral epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have been as the 2nd-line treatment for NSCLC. It is widely accepted that some clinicopathologic characteristics (female, nonsmoking status, Asian, and EGFR mutations) are the main clinical positive predictive factors when using EGFR-TKIs. The el der patients often suffer from deterioration of performance status (PS). The side reaction caused by chemotherapy is serious and unavoidable. For the elder patients with positive predictive factors and poor PS, there is no report about anti-NSCLC using gefitinib as the 1st-line treatment. We report the case of an 84-year-old woman with diffuse bone metastases from lung cancer. She received oral gefitinib 150 mg / day, combined with three dimensional conformal radiation therapies (3DCRT). A total tumor dose of 36Gy / 12fractions was delivered to the tumor bed and localized metastatic bone pain areas, respectively. After concurrent gefitinib-3DCRT, gefitinib was continued as maintenance therapy. She experienced total regression of the metastases under gefitinib treatment for 30 months. Gefitinib therapy provided effective anti-tumor results. Therefore, for NSCLC patients of multiple metastases with favorable predictive factors such as EGFR mutations, adenocarcinoma, Asian, female gender and nonsmoking status, we suggest that gefitinib may become the 1st-line treatment even with poor PS. doi:10.4021/jmc513w
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".