Optimal Use of Prognostic Factors in Non-Hodgkin Lymphoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The management of non-Hodgkin lymphoma is complicated by wide heterogeneity within recognized subtypes. Patients with supposedly similar diagnoses can have remarkably varied clinical presentations, molecular profiles and clinical outcomes. Reliable prognostic markers could allow the identification of patient subsets that may benefit from alternate approaches. Historically, a large number of clinical and molecular prognostic factors have been elucidated. However, the recent introduction of new therapies such as monoclonal antibodies has revolutionized treatment practices and greatly improved outcomes. This has called into question the value of previously recognized prognostic factors that need to be revalidated in the era of immunochemotherapy. It would appear that the commonly used clinical indices (IPI and FLIPI) retain predictive capacity, although they may have limited ability to identify a very poor outcome group. Currently there are no molecular markers that have been revalidated and shown to retain significance in the setting of current treatment practices for diffuse large B-cell lymphoma or follicular lymphoma. The biologic insights provided by molecular studies should allow for more targeted therapies to be developed, which will increase treatment choice and the possibility of tailored therapy in the future. It is imperative that future steps forward be made in the context of well-designed clinical trials with prospective correlative studies of clinical and biologic markers. This will allow us to continuously assess outcome predictors in the context of treatment change and to rationally design tailored treatment algorithms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it