HEMATOLOGICAL MALIGNANCIES IN CHERNOBYL CLEAN-UP WORKERS (1996-2010)
Bibliographic record
Abstract
Background : Even in 25 years after Chernobyl catastrophe, the interpretation of the findings on leukemia risk among Chernobyl clean-up workers is still a point of much controversy. Precise diagnosis of the main types of hematopoietic malignancies according to FAB classification and new WHO classification and comparison of these data with that in the general population will be helpful in estimating the relative contribution of the radiation factor to the overall incidence of such pathologies. Methods : The data on 295 consecutive cases of malignant tumors of hematopoietic and lymphoid tissues in Chernobyl clean-up workers diagnosed in 1996-2010 are given in comparison with the data of 2697 consecutive patients of general population of the same age group. The complex of diagnostic techniques was used including morphology and cytochemistry of bone marrow and peripheral blood cells as well as immunocytochemical techniques (APAAP, LSAB-AP) and the broad panel of monoclonal antibodies to lineage specific and differentiation antigens of leukocytes. Results: All the main forms of tumors of hematopoietic and lymphoid tissues were diagnosed among clean-up workers under study in 10-25 years after Chernobyl catastrophe including myelodysplastic syndromes (MDS), acute leukemias (ALL and AML), chronic myelogenous leukemia (CML) and other chronic myeloproliferative neoplasms, chronic lymphocytic leukemia (B-CLL) and other chronic lymphoproliferative diseases of B and T cell origin. Among clean-up workers with hematopoietic malignancies, MDS percentage tended to exceed that in the group of patients representing the general population examined at the same period (5.42% vs. 3.70%). Among 46 AML cases in clean-up workers, leukemia was preceded by MDS in seven patients. Also CML percentage was higher in group of patients representing clean-up workers (9.01% vs. 6.59%). B-CLL was a predominant form of hematopoietic malignancies in clean-up workers under study (26.10%). Nevertheless, B-CLL percentage in patients of clean-up workers group did not differ significantly from that in the patients of general population. The multiple myeloma percentage in the group of patients belonging to clean-up workers in our study was higher than in the patients of general population (6.44% vs. 4.0%). Conclusions: The verified diagnosis of tumors of hematopoietic and lymphoid tissues according to the up-to-date classifications (EGIL, WHO) could be the prerequisite for further molecular genetic and analytical epidemiology study of leukemias that may be related to Chernobyl catastrophe. Keywords: Chernobyl clean-up workers, leukemias, cytochemistry, immunophenotyping
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".