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Record W2094252783 · doi:10.3109/10428194.2013.827785

Preservation of lower incidence of chronic lymphocytic leukemia in Chinese residents in British Columbia: a 26-year survey from 1983 to 2008

2013· article· en· W2094252783 on OpenAlexaffabout
Vivien Mak, Dkm Ip, Oscar Mang, Chinmay B. Dalal, Steven J.T. Huang, Alina S. Gerrie, Tanya L. Gillan, Khaled M. A. Ramadan, Cynthia L. Toze, Wing‐Yan Au

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2013
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsSt. Paul's HospitalVancouver General HospitalUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsChronic lymphocytic leukemiaIncidence (geometry)DemographyMedicineCancer registryCancer incidencePopulationStandardized rateChinese populationLeukemiaInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

The incidence of chronic lymphocytic leukemia (CLL) in the Asian population is up to 10 times lower than that in Caucasians. Studies on CLL in Asian residents in North America may help to determine the relative genetic and environmental causes of such a difference. Computerized records of CLL incidence from the combined British Columbia (BC) databases (n = 2736) and the Hong Kong Cancer Registry (HKCR, n = 572) were traced. Ethnic Chinese cases of CLL in BC were identified (n = 35). The world age standardized rates (WASRs) of CLL (per 100 000) were calculated in BC (1.71), HK (0.28) and BC Chinese (0.4), respectively. Using standard incidence ratios (SIRs), the observed BC Chinese case number was comparable to the figure projected from HK rates (SIR 1.3, p = 0.1) but significantly lower than the figure following BC rates (SIR 0.22, p < 0.0001). The difference was maintained over both genders, in all age groups and through the years. Our data over three decades suggest that genetic factors outplay environmental factors to give lower CLL rates in Chinese.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.257
Teacher spread0.246 · 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 teacher head, not a consensus.

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

Citations35
Published2013
Admission routes2
Has abstractyes

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