Second primary cancers among 109 000 cases of non-Hodgkin's lymphoma
Bibliographic record
Abstract
An analysis of other primary cancers in individuals with non-Hodgkin's lymphoma (NHL) can help to elucidate this cancer aetiology. In all, 109 451 first primary NHL were included in a pooled analysis of 13 cancer registries. The observed numbers of second cancers were compared to the expected numbers derived from the age-, sex-, calendar period- and registry-specific incidence rates. We also calculated the standardised incidence ratios for NHL as a second primary after other cancers. There was a 47% (95% confidence interval 43-51%) overall increase in the risk of a primary cancer after NHL. A strongly significant (P<0.001) increase was observed for cancers of the lip, tongue, oropharynx*, stomach, small intestine, colon*, liver, nasal cavity*, lung, soft tissues*, skin melanoma*, nonmelanoma skin*, bladder*, kidney*, thyroid*, Hodgkin's lymphoma*, lymphoid leukaemia* and myeloid leukaemia. Non-Hodgkin's lymphoma as a second primary was increased after cancers marked with an asterisk. Patterns of risk indicate a treatment effect for lung, bladder, stomach, Hodgkin's lymphoma and myeloid leukaemia. Common risk factors may be involved for cancers of the lung, bladder, nasal cavity and for soft tissues, such as pesticides. Bidirectional effects for several cancer sites of potential viral origin argue strongly for a role for immune suppression in NHL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".