Pembrolizumab and its effectiveness in treating non-small cell lung cancer
Bibliographic record
Abstract
Non-small cell lung cancer accounts for approximately 80-90% of all lung cancers worldwide. Causes of this type of cancer may vary as point mutations, prolonged exposure to carcinogens, and many more could lead to the growth of tumor cells in the lungs. It has traditionally been treated with chemotherapy, which was considered the most effective method, yet the emergence and prevailing rapid development in the field of immunotherapy has proven to be an alternatively effective treatment for certain types. The basic principle of immunotherapy is modifying particular components of the immune system for enhanced efficiency and potential. Immune checkpoints inhibitors are a group of immunotherapy that involve the interference of specific biological/cellular pathways associated with the false signalling of T cell deactivation by cancer cells. Pembrolizumab, also known as Keytruda, is a humanized monoclonal antibody commonly used to treat lung cancer. It is an anti-PD-1 inhibitor that targets the PD-1/PD-L1 pathway in T cells. Immunotherapy takes advantage of the rise of personalized medicine by targeting a specific immune mechanism and selecting the appropriate medications.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".