The Role of Surrogate Markers in the Clinical Development of Antiretroviral Therapy: A Model for Early Evaluation of Targeted Cancer Drugs
Bibliographic record
Abstract
Both CD4 cell counts and measurements of plasma HIV-1 RNA (i.e. viral load) have become established surrogate markers for predicting treatment and disease outcome in HIV infection, and are instrumental for the evaluation of new antiretroviral drugs in clinical trials. Recently, HIV drug-resistance testing has also become available and has been shown to have prognostic value in providing guidance with antiretroviral therapy. The identification of robust surrogate markers is also an essential requirement for the clinical development of targeted anticancer agents, which unlike their cytotoxic counterparts, are often devoid of the toxicities that have been traditionally used to monitor the efficacy of chemotherapy. In particular, biological or molecular markers that are predictive of a drug effect need to be integrated into early efficacy trials of targeted therapies in order to confirm that the drug is in fact "hitting" the intended target. The full clinical significance of many of the altered cell types or polymorphisms, which are selected by cytostatic agents, remains to be elucidated. However, molecular genotyping of these targets, akin to drug resistance testing for HIV infection, may constitute an important strategy to assist with the selection and monitoring of targeted chemotherapy in cancer patients. Thus, lessons from HIV/AIDS on the value of surrogate makers may assist with the development and optimization of targeted cancer therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".