Clinical implications of antiangiogenic therapies.
Bibliographic record
Abstract
The most common subtype of aggressive non-Hodgkin's lymphoma is diffuse large B-cell lymphoma (DLBCL). Diffuse large B-cell lymphoma represents a heterogeneous entity, with 5-year overall survival rates ranging from 26% to 73%. Microarray gene expression studies have confirmed that biologically distinct subgroups exist within DLBCL, and can be correlated with outcome. Initial management is usually guided by stage of disease at presentation. Approximately 25% of patients with DLBCL present with limited-stage disease and are treated with combined-modality therapy (brief chemotherapy and involved-field radiation). Most patients present with advanced-stage disease and require treatment with an extended course of chemotherapy. The CHOP (cyclophosphamide, doxorubicin HCl, vincristine [Oncovin], prednisone) chemotherapy regimen has been the mainstay of therapy since its development in the 1970s, as more intensive chemotherapy regimens failed to show additional benefit. The era of monoclonal antibodies has transformed treatment practices for aggressive lymphoma and has led to a significant improvement in outcome. A randomized trial comparing the use of rituximab (Rituxan), a chimeric anti-CD20 IgG1 monoclonal antibody, combined with CHOP chemotherapy vs CHOP chemotherapy alone for elderly patients with advanced-stage DLBCL demonstrated a significant benefitfor the combination approach. This finding has now been confirmed in two additional randomized, controlled trials and a population-based analysis, making CHOP and rituximab the standard of care for all newly diagnosed patients with DLBCL. Despite this advance, newer therapies are needed and many are under active investigation. The insights gained from molecular techniques such as gene expression profiling should permit identification of additional lymphoma-specific therapeutic targets and the development of novel agents that take into account underlying biology and allow for greater tailoring of therapy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".