Methods for Sample Acquisition and Processing of Serial Blood and Tumor Biopsies for Multicenter Diffuse Large B-cell Lymphoma Clinical Trials
Bibliographic record
Abstract
Increasingly, targeted therapies are being developed to treat malignancies. To define targets, determine mechanisms of response and resistance, and develop biomarkers for the successful investigation of novel therapeutics, high-quality tumor biospecimens are critical. We have developed standard operating procedures (SOPs) to acquire and process serial blood and tumor biopsies from patients with diffuse large B-cell lymphoma enrolled in multicenter clinical trials. These SOPs allow for collection and processing of materials suitable for multiple downstream applications, including immunohistochemistry, cDNA microarrays, exome sequencing, and metabolomics. By standardizing these methods, we control preanalytic variables that ensure high reproducibility of results and facilitate the integration of datasets from such trials. This will facilitate translational research, better treatment selection, and more rapid and efficient development of new drugs. See all the articles in this CEBP Focus section, "Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology."
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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 | 0.035 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.015 | 0.018 |
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".