An Epidemiological Model to Estimate the Prevalence of Diffuse Large B-Cell Lymphoma in the United States
Bibliographic record
Abstract
BACKGROUND: Prevalence is reflective of disease incidence and survival, and defined as the number of patients living with active disease. In diseases such as diffuse large B-cell lymphoma (DLBCL) with treatments with curative potential, a proportion of patients are cured, leading to a need for accurate, contemporary estimates of DLBCL prevalence to gauge the impact of the rapidly emerging treatment landscape. METHODS: Data from Surveillance, Epidemiology, and End Results (SEER) from 2000-2018 were utilized to develop an epidemiological model of incidence, survival, and cure, to estimate the current prevalent DLBCL population requiring active management in the United States (US). A variety of estimates were explored regarding cure rate and timing, based on a companion analysis of MarketScan data for treatment patterns and survival in incident DLBCL patients, and conditional survival analysis of SEER data. RESULTS: Across scenarios, with estimated cure ranging from 52.8% and 68.9%, and timing of cure ranging from 1 and 20 years post diagnosis, the estimated prevalence ranged from 63,883 to 142,889. With an assumption of no cure, estimated prevalence was 179,475. DISCUSSION: Prevalence estimates of DLBCL varied almost 3-fold, depending on specific cure adjustments made. Further understanding of DLBCL prevalence, for newly diagnosed and relapsed and/or refractory disease, is important to characterize the impact of emerging treatment options and related health care burden.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".