P1-311 Lymphohematopoietic cancers and benzene: a pooled analysis of petroleum workers
Bibliographic record
Abstract
Introduction There are few quantitative studies on the effect of relatively low benzene concentrations on risks of specific lymphohematopoietic cancer subtypes. Three nested case-control studies among petroleum workers in Australia, Canada and the UK have been updated and pooled to provide greater precision. Methods To improve disease subtype classification, pathology records were obtained; two pathologists reviewed these and classified every case according to traditional and WHO classification schemes. Quantitative exposure estimates were also compared across studies to ensure that any differences in these estimates were justified. Statistical analyses employed conditional logistic regression models with flexible penalised cubic regression spline components. Results Updates identified 170 additional cases giving a total of 370, sufficient for separate analyses by leukaemia subtypes, myelodysplastic syndrome (MDS), and myeloproliferative disease (MPD). Review of source records by pathologists resulted in changes to the underlying disease subtypes for certain leukaemia cases; pre-existing diseases such as MDS were identified; secondary polycythaemia cases were identified and excluded. Risks for acute myeloid leukaemia (AML) tended to increase as categorical benzene exposure increased when pooling the original data from the previously published studies using both the original and revised exposure assessment. Dose-response results from the updated pooled data for MDS, MPD, AML and chronic myeloid leukaemia, and chronic lymphoid leukaemia will be presented from the updated dataset. Conclusions This pooled study benefited from careful reconsideration of benzene exposure estimates and disease classification procedures, improving the precision of risk estimates of benzene exposure for leukaemia and other disease subtypes.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.011 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".