Role of Topotecan in Non-Small Cell Lung Cancer: A Review of Literature
Bibliographic record
Abstract
Topotecan (TPT), a chemotherapeutic agent, is a topoisomerase-I inhibitor. Topoisomerase-I is a nuclear enzyme that relieves torsion strain in DNA by opening single strand breaks which helps in DNA replication. TPT inhibits this enzyme, thus preventing DNA replication and causes cell death. TPT has demonstrated to have broad spectrum of antitumor activity in tumors like cervical, ovarian, endometrial and small cell lung cancers (SCLCs). The intravenous (IV) formulation of the drug is currently approved by the US Food and Drug Administration for the treatment of patients with SCLC and ovarian cancer at a dose of 1.5 mg/m 2 administered daily for five consecutive days, with treatment cycles repeated every 3 weeks. TPT has shown some promising activity in the treatment of non-small cell lung cancer (NSCLC) with favorable side effect profile. Several clinical trials have been conducted with TPT in either IV or oral formulation for the treatment of NSCLC as a first or second-line treatment. Here we reviewed all the clinical trials done with TPT to date in the treatment of NSCLC both as a single-agent and combination therapy. World J Oncol. 2015;6(5):429-436 doi: http://dx.doi.org/10.14740/wjon950e
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".