Companion diagnostics and the age of personalized medicine in oncology
Bibliographic record
Abstract
Companion diagnostics are tests which are used to guide targeted therapies. They represent the application of knowledge of unique sensitivities of hosts or diseases. The FDA approved the first companion diagnostic-related drug, trastuzumab (Herceptin), in 1998. Trastuzumab is specifically effective in human epidermal growth factor receptor 2 (HER2) positive cancers, which are associated with poor outcomes with conventional cytotoxic therapy. Since trastuzumab, many other companion diagnostics have been brought to market. There are both health and economical advantages to developing companion diagnostics. With personalized therapy, patients will experience fewer and less severe side effects, and patients are more likely to have positive outcomes. Companies seeking to approve companion diagnostics will be able to recruit fewer participants for efficacy trials, leading to decreased development costs. Challenges to the future of companion diagnostics include preventing off-label usage of drugs. Additionally, the likely expensive cost of companion diagnostics, as in the example of ivacaftor (Kalydeco), will raise questions on how these drugs will be paid for.
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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".