Bibliographic record
Abstract
Trifluridine/tipiracil (TAS-102) is a newer generation chemotherapy that has been approved for the later-line treatment of metastatic colorectal and gastric/gastroesophageal adenocarcinomas. The oral drug provides a modest benefit of prolongation of survival over placebo in pretreated patients with these cancers with acceptable toxicity. Studies have shown rare objective responses (2-4%), and the disease control rates were 44% in both colorectal and gastric cancer randomized trials. Thus, the majority of patients progress through treatment and are burdened by toxicities. To better characterize the sub-group of patients with a higher probability of benefit from trifluridine/tipiracil, predictive biomarkers have been sought using data from randomized trials as well as from non-randomized trials and real-world series. Biomarkers examined include clinical characteristics of the patients, laboratory tests, and tumor derived biomarkers. These studies show that early neutropenia on treatment, and ratios of leukocyte subsets, are potential biomarkers able to predict trifluridine/tipiracil benefit. Combinations of laboratory values and clinical characteristics and proteins involved in trifluridine transport and activation have been examined with initial positive results.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".