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Record W4405651961 · doi:10.1177/15593258241303476

INWORKS and Hiroshima/Nagasaki Leukaemia Results

2024· article· en· W4405651961 on OpenAlexaff
Ken Robert Chaplin

Bibliographic record

VenueDose-Response · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsChronic myeloid leukaemiaMyeloid leukaemiaMedicineChildhood leukaemiaLeukemiaRadiation exposureNuclear medicineCumulative doseInternal medicinePediatrics

Abstract

fetched live from OpenAlex

The Hiroshima/Nagasaki (H/N) studies by the Radiation Effect Research Foundation have not shown increased leukaemia for acute doses below 200 milli-gray (mGy). By contrast the INWORKS study of leukaemia in workers stated: "This study provides strong evidence of positive associations between protracted low-dose radiation exposure and leukemia". The INWORKS study also claimed increased leukaemia, not including Chronic Lymphocytic Leukaemia, at cumulative occupational doses of less than 100 mGy. This is contrary to the expectation that the H/N studies would show more severe effects than the worker study because the doses were acute instead of chronic and because the H/N studies included children who had higher rates of radiation induced leukaemia than adults. This paper shows that the INWORKS leukaemia study is consistent with the H/N studies in showing no increase in leukaemia in the low-dose range. In addition, any increase in leukaemia is confined to Chronic Myeloid Leukaemia, possibly from high radiation dose-rates or chemicals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0150.002

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.

Opus teacher head0.016
GPT teacher head0.307
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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