General Biomarker Recommendations for Lymphoma
Bibliographic record
Abstract
Lymphoid malignancies are a heterogeneous group of tumors that have distinctive clinical and biological behaviors. The increasing prevalence of disease reflects both treatment advances and the fact that some of these tumors are indolent. The ability to determine treatment needs at diagnosis remains problematic for some of the tumors, such as in follicular lymphomas. Major clinical advances will likely depend on precision oncology that will enable identification of specific disease entities, prognostic determination at diagnosis, and identification of precise therapeutic targets and essential pathways. However, refinement in diagnostic evaluation is an evolving science. The ability to determine prognosis at diagnosis is variable, and for many of the lymphoid malignancies prognosis can only be made after initial treatment. Clinical trials that aim to evaluate specific features of these diseases are required in order to advance clinical practice that meaningfully addresses this important public health challenge. Herein, we describe the process and general recommendation from the National Cancer Institute (NCI) clinical trials planning meeting in November 2014 to address clinical trial design and biomarker proposals in the context of NCI-supported lymphoma clinical trials in the National Clinical Trials Network.
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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 | 0.114 | 0.228 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.016 | 0.006 |
| Research integrity | 0.049 | 0.036 |
| Insufficient payload (model declined to judge) | 0.023 | 0.026 |
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".