Early‐stage Hodgkin lymphoma in the modern era: simulation modelling to delineate long‐term patient outcomes
Bibliographic record
Abstract
F-fluorodeoxyglucose positron emission tomography response-adaption. The model incorporated 3-year progression-free survival (PFS), probability of cure with/without relapse, frequency of severe late effects (LEs), and 35-year probability of LEs. Furthermore, we generated estimates for quality-adjusted life years (QALYs) and unadjusted survival (life years, LY) and used model projections to compare outcomes for CMTversusCA for two index patients. Patient 1: a 25-year-old male with favourable ESHL (stage IA); Patient 2: a 25-year-old female with unfavourable ESHL (stage IIB). Sensitivity analyses assessed the impact of alternative assumptions for LE probabilities. For Patient 1, CMT was superior to CA (CMT incremental gain = 0·11 QALYs, 0·21 LYs). For Patient 2, CA was superior to CMT (CA incremental gain = 0·37 QALYs, 0·92 LYs). For Patient 1, the advantage of CMT changed minimally when the proportion of severe LEs was reduced from 20% to 5% (0·15 QALYs, 0·43 LYs), whereas increasing the severity proportion for Patient 2's LEs from 20% to 80% enhanced the advantage of CA (1·1 QALYs, 6·5 LYs). Collectively, this detailed simulation model quantified the long-term impact that varied host factors and alternative contemporary treatments have in ESHL.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".