Influence of Age on Long-term Net Survival Benefit for Early-stage MALT Lymphomas Treated With Radiotherapy: a SEER Database Analysis (2000-2015)
Bibliographic record
Abstract
Abstract BACKGROUND Given the lower incidence of lymphoma-related death but higher background mortality in patients with early-stage mucosa-associated lymphoid tissue (MALT) lymphoma, it is critically important to examine how age affects a treatment’s survival benefit. METHODS 9,467 patients with early-stage MALT lymphoma in the Surveillance, Epidemiology, and End Results (SEER) database treated between 2000-2015 were extracted and analyzed. Primary therapy was classified as radiotherapy (n = 3,407), chemotherapy (n = 1,294), and other/unknown treatments including observation (n = 4,766). Inverse probability of treatment weighting (IPTW) was conducted to balance baseline characteristics between groups. Relative survival (RS), standardized mortality ratio (SMR), and transformed Cox regression were conducted to compare survival differences between treatment modalities by controlling for the background mortality. Radiotherapy–age interaction was examined.RESULTS Across age-groups, early-stage MALT lymphoma patients were at lower risk of lymphoma-related death than death due to other causes. The 10-year overall survival (OS, 73.8%) and RS (96.6%) rates were significantly higher, and the SMR (1.14) significantly lower, with radiotherapy than with chemotherapy (OS, 61.7%; RS, 86.4%; SMR, 1.54; P < 0.001) or other/unknown treatments (OS, 61.1%; RS, 87.2%; SMR, 1.41; P < 0.001). By multivariable analysis and IPTW, radiotherapy remained an independent predictor of better RS (HR 0.81, 95%CI, 0.73-0.89; P < 0.001). A significant interaction between age and radiotherapy was identified for both RS (Pinteraction = 0.016) and OS (Pinteraction = 0.024), indicating greater benefit in young adults.CONCLUSION Radiotherapy can provide significant survival benefit in early-stage MALT lymphoma, especially in young adults.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".