Time to diagnosis and treatment in lymphoma and implications for health-related outcomes: a systematic review
Bibliographic record
Abstract
Timely diagnosis and treatment are important in lymphoma care. This systematic review examined articles published up to April 2025 which reported intervals from symptom onset to treatment initiation and a subset examining associations with health-related outcomes. Of 11,606 articles screened, 67 were included (23 reported associations). Significant heterogeneity was noted, with 27 intervals reported across various lymphoma subtypes. Methodological issues included poor reporting of interval variability, small sample sizes, and arbitrary interval categorization. Commonly reported intervals included symptom onset to diagnosis (articles; median range), (27; 26-217 days), symptom onset to first presentation (23; 9-91 days), first presentation to diagnosis (17; 15-126 days), and diagnosis to treatment start (25; 1-42 days). Most association studies considered treatment interval and survival, finding inconsistencies. Only few examined the length of diagnostic or patient interval impacts on health outcomes. Future research should apply the Aarhus Checklist - a tool designed to enhance precision and transparency in early cancer diagnosis research to improve the consistency and quality of interval reporting. Further, non-linear interval-survival associations should be explored to capture paradoxical effects.
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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 | 0.012 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".