Bibliographic record
Abstract
Leukaemia Leukaemia is responsible for approximately 2 per cent of all cancers, with an incidence of about 8 per 100 000 in the UK. Acute myeloid and lymphoblastic leukaemias (AML and ALL) account for about 1 per cent of all cancers and 1.5 per cent of cancer deaths. The age incidence of leukaemia shows two peaks, in childhood and in the elderly. Genetic factors are not considered to have a prominent role in the pathogenesis of acute leukaemias or in chronic myeloid leukaemia, but have been implicated in chronic lymphocytic leukaemia (CLL). Gunz et al. (1975) studied the incidence of leukaemia in relatives of 909 patients with leukaemia. The overall incidence of leukaemia in first-degree relatives was three times higher than expected although only 2 per cent of patients had a first-degree relative with leukaemia. Among the main subtypes of leukaemia, an increased risk to relatives was most marked in chronic lymphocytic leukaemia, less so in acute leukaemias and absent in chronic myeloid leukaemia. When familial clusters of leukaemia have been reported, the type of leukaemia in individual relatives is not always concordant (Lee et al., 1987). Familial leukaemia does not necessarily indicate a genetic cause, and shared exposure to an environmental leukaemogen also needs to be considered, particularly in childhood acute leukaemias. Genetic disorders that have been associated with a predisposition to leukaemia are shown in Table 9.1 and discussed in detail in part three. Genetic disorders are thought to account for only 3 per cent of childhood leukaemia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.243 | 0.144 |
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".