A Literature Review of Single Agent Treatment of Multiply Relapsed Aggressive Non-Hodgkin's Lymphoma
Bibliographic record
Abstract
To analyze the available literature describing the treatment of relapsed aggressive non-Hodgkin's lymphoma (NHL) with single-agent chemotherapies, several comprehensive electronic and manual inspections of the literature were performed for the period from 1966 to the present. Each paper was examined to capture the following data: study type; patient demographics and characteristics; study endpoints, including responses, and method used to evaluate response; toxicities, and the power of the study. A wide variety of single-agent protocols continue to be studied, indicating no currently accepted standard therapy in this patient population. Reported response rates varied between 0 and 67%. The majority of trials were small, uncontrolled studies that used widely varying inclusion/exclusion criteria and had limited reporting of histology, response, prior treatments, and other key parameters. We were able to find only four agents, etoposide, vincristine, vinorelbine and possibly rituximab, with sufficient reproducible evidence to suggest greater than 30% activity (CR + PR rate) when given to patients with second or greater relapse of aggressive NHL. Consequently, the usefulness of the agents in these reports remains to be established in larger trials with more detailed reporting. The advantages that would be brought by an active non-myelosuppressive agent for patients having this condition emerge clearly from this review.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".