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Incidence and spectrum of non‐Hodgkin lymphoma in Chinese migrants to British Columbia

2005· article· en· W1982060371 on OpenAlexaffabout
W.Y. Au, Randy D. Gascoyne, R.D. Klasa, Joseph M. Connors, R. P. Gallagher, Nhu D. Le, F Loong, Chi-Ching Law, Raymond Liang

Bibliographic record

VenueBritish Journal of Haematology · 2005
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsIncidence (geometry)MedicineLymphomaDemographyStandardized rateFollicular lymphomaNon-Hodgkin's lymphomaPopulationInternal medicinePediatricsEpidemiologyEnvironmental health

Abstract

fetched live from OpenAlex

The incidence and spectrum of non-Hodgkin lymphoma (NHL) differ between the Chinese and Caucasian populations. Using population-based registries, we studied the pattern of NHL in Chinese migrants to British Columbia (BC). The records of all NHL cases of Chinese descent diagnosed between 1980 and 1997 were retrieved. Age-standardized incidences were calculated by 5-year intervals in terms of age and calendar years and the relative rates were compared between the migrant, Hong Kong and BC populations. The histological distribution of NHL was compared with 4500 consecutive NHL cases diagnosed in the two populations. A total of 211 cases of migrant NHL were identified, with an age-standardized incidence rate of 7.11 per 100 000 per year, compared with the Hong Kong and BC rates of 7.91 [standardized incidence ratio (SIR) = 0.86, P = 0.01] and 11.88 (SIR = 0.56, P < 0.01). The standardized rates of follicular lymphoma remained low, but the incidence of gastric and nasal natural killer/T lymphomas in migrants were lower than expected. Genetic factors appeared to be stronger than environmental factors in governing the overall incidence of NHL in Chinese. However, certain subtypes of lymphoma may show decreased rates in migrants because of environmental factors.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.260
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.253
Teacher spread0.248 · 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.

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

Citations43
Published2005
Admission routes2
Has abstractyes

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