Shorter Diagnosis-to-Treatment Interval in Diffuse Large B-Cell Lymphoma is Associated With Inferior Overall Survival in a Large, Population-Based Registry
Bibliographic record
Abstract
BACKGROUND: Because of prolonged screening requirements, patient and time-dependent selection have been proposed as potential biases in clinical trials. The screening process may exclude patients with a need for emergent treatment (and a short period from diagnosis to treatment initiation [DTI]). We explored the impact of DTI on overall survival (OS) in a population-based cohort of patients with diffuse large B-cell lymphoma (DLBCL). PATIENTS AND METHODS: Using population-based administrative databases in Ontario, Canada, we identified adults aged ≥18 years with DLBCL treated with rituximab-based chemotherapy for curative intent between January 2005 and December 2015. Cox regression and multivariable analyses were presented to evaluate the impact of time from DTI on OS, controlling for relevant covariates. RESULTS: We identified 9,441 patients with DLBCL in Ontario; median age was 66 years, 53.6% were male, median number of comorbidities (Johns Hopkins aggregated diagnosis groups) was 10 (interquartile range [IQR], 8-13), and median DTI was 37 days (IQR, 22-61). Between treatment initiation and study end, 43% of patients died (median OS, 1 year; IQR, 0.4-2.8 years). Shorter DTI was a significant predictor of mortality (P<.001). Compared with the shortest DTI period of 0-18 days, those who commenced therapy at 19-29 days (hazard ratio [HR], 0.75; 95% CI, 0.68-0.84), 30-41 days (HR, 0.70; 95% CI, 0.63-0.78), 42-57 days (HR, 0.52; 95% CI, 0.46-0.58), and 58-180 days (HR, 0.52; 95% CI, 0.47-0.58) had improved survival. Increasing age (HR, 1.03; 95% CI, 1.03-1.04), male sex (HR, 1.23; 95% CI, 1.14-1.32), and increasing number of comorbidities (HR, 1.12; 95% CI, 1.11-1.13) were associated with inferior survival. CONCLUSIONS: Among patients with DLBCL, shorter DTI was associated with inferior OS. Therefore, DTI may represent a surrogate marker for aggressive biology. Clinical trials with lengthy screening periods are likely creating a time-dependent patient selection bias.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".