Bibliographic record
Abstract
Improving our ability to predict which patients will respond to epidermal growth factor receptor (EGFR) inhibitors and, indeed, all molecularly targeted agents will be critical if we hope to improve the cost-benefit ratio of these expensive drugs. In this regard, cost may be measured in terms of monetary cost but also in terms of the cost of toxicity experienced by patients, both those who respond and those who do not respond to the targeted therapy. Valerius 1 raisestwoimportantissuesinhisletter.First,hepoints outthatEGFRmonoclonalantibodiesmayhaveanaddedmechanism of action, namely antibody-dependent cellular cytotoxicity (ADCC), that would not be expected with EGFR tyrosine kinase inhibitors (TKIs). It has been suggested that ADCC may in part explain why almost all of the trials of cetuximab added to chemotherapy for non‐small-cell lung cancer (NSCLC) demonstrated some degree of added benefit, when this clearly was not the case for any trial in which EGFR TKIs were added to chemotherapy. If ADDC does contributesignificantlytoresponse,immunoglobulinG1antibodies such as cetuximab might be expected to be more effective than immunoglobulin G2 antibodies such as panitumumab. However, both of these antibodies have demonstrated activity in patients with colorectal cancer, 2 and for both antibodies, KRAS mutation status has been predictive of a differential survival benefit. 3 Direct
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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.028 | 0.045 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".