Mortality among patients with diffuse large B-cell lymphoma and mental disorders: a population-based study
Bibliographic record
Abstract
BACKGROUND: Mental disorders have been reported in patients with diffuse large B-cell lymphoma (DLBCL), but studies examining their association with mortality are lacking. METHODS: We conducted a population-based study using linked administrative health-care databases from Ontario, Canada. All patients with DLBCL 18 years of age or older treated with rituximab-based therapy between January 1, 2005, and December 31, 2017, were identified and followed until March 1, 2020. Mental disorders were defined as either preexisting or postdiagnosis (after lymphoma treatment initiation). Cox proportional hazards models were used to estimate the adjusted hazard ratio (HR) between mental disorders and 1-year and all-cause mortality while controlling for covariates. RESULTS: We identified 10 299 patients with DLBCL. The median age of the cohort was 67 years; 46% of patients were female, and 28% had a preexisting mental disorder. At 1-year follow-up, 892 (9%) had a postdiagnosis mental disorder, and a total of 2008 (20%) patients died. Preexisting mental disorders were not associated with 1-year mortality (adjusted HR = 1.06, 95% confidence interval [CI] = 0.96 to 1.17, P = .25), but postdiagnosis disorders were (adjusted HR = 1.51, 95% CI = 1.26 to 1.82, P = .0001). During a median follow-up of 5.2 years, 2111 (22%) patients had a postdiagnosis mental disorder, and 4084 (40%) patients died. Both preexisting and postdiagnosis mental disorders were associated with worse all-cause mortality (preexisting adjusted HR = 1.12, 95% CI = 1.04 to 1.20, P = .0024; postdiagnosis adjusted HR = 1.63, 95% CI = 1.49 to 1.79, P < .0001). CONCLUSIONS: Patients with DLBCL and mental disorders had worse short-term and long-term mortality, particularly those with postdiagnosis mental disorders. Further studies are needed to examine mental health service utilization and factors mediating the relationship between mental disorders and inferior mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".