The emerging role of nimotuzumab in the treatment of non-small cell lung cancer
Bibliographic record
Abstract
William Boland1, Gwyn Bebb2,31Department of Anatomy and Cell Biology, McGill University, Montreal, Quebec, Canada; 2Tom Baker Cancer Centre, Calgary, Alberta, Canada; 3University of Calgary, Calgary, Alberta, CanadaAbstract: Current non-small cell lung cancer (NSCLC) chemotherapy and radiotherapy regimens, although showing definite survival benefit, still leave patients with a disappointing 15% 5-year overall survival rate. Because of the need to improve traditional outcomes, research has focused on identifying specific tumorigenic pathways that may serve as therapeutic targets. The most successful strategies to date are those aimed at the epidermal growth factor receptor (EGFR), which is found to be upregulated in 40%–80% of NSCLC. Several tyrosine kinase inhibitors and monoclonal antibodies (mAbs) have been developed that inhibit the EGFR receptor and have demonstrated clinical benefit in trials as single agents and in combination regimens. Here we discuss one such agent, the mAb nimotuzumab, the background of its development, its clinical experience in NSCLC thus far, and the rationale for expanding its use to other NSCLC treatment settings.Keywords: non-small cell lung cancer, chemotherapy, radiotherapy, overall survival, EGFR
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".