[Non-small-cell lung cancer (NSCLC) in an elderly patient--a case of squamous cell carcinoma successfully treated with chemotherapy using vinorelbine].
Bibliographic record
Abstract
We report an elderly patient with squamous cell carcinoma who was successfully treated with chemotherapy using vinorelbine. A 76-year-old man was referred to our hospital for evaluation of a nodular shadow in the left lung. Chest CT scam showed a 3-cm tumor shadow in left S9 and a 1-cm small nodule in right S2. Transbronchial lung biopsy yielded a diagnosis of squamous cell carcinoma. The clinical stage was IV (cT2N2M1). The patient first underwent chemotherapy consisting of cisplatin (CDDP) 80 mg/m2 on day 1 and vinorelbine (VNB) 20 mg/m2 on days 1, 8, and 15, which generated tumor shrinkage of 48% as well as transient elevation of grade 1 in serum creatinine. The 2 cycles of chemotherapy using vinorelbine only (VNB 20 mg/m2 on days 1, 8, 15) produced a tumor reduction of 70% with grade-1 decrease of granulocytes. The low grade of toxicity enabled us to treat the patient in our outpatient office for the second cycle of the regimen. This case suggests that chemotherapy using low-dose vinorelbine might be suitable to treat elderly patients with NSCLC in outpatient settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".