Real world data as a key element in precision medicine for lymphoid malignancies: potentials and pitfalls
Bibliographic record
Abstract
Molecular genetic studies of lymphoma have led to refinements in disease classification in the most recent World Health Organization update. Nevertheless, a 'one-size-fits-most' treatment strategy based on morphology remains widely used for lymphoma despite significant molecular heterogeneity within histopathologically-defined subtypes. Precision medicine aims to improve patient outcomes by leveraging disease- and patient-specific information to optimise treatment strategies, but implementation of precision medicine strategies is challenged by the biological diversity and rarity of lymphomas. In this review, we explore existing and emerging real-world data sources that can be used to facilitate the development of precision medicine strategies in lymphoma. We provide illustrative examples of the use of real-world analyses to refine treatment strategies, provide comparators for clinical trials, improve risk-stratification to identify patients with unmet clinical needs and describe long-term and rare toxicities.
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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 | 0.090 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".