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Record W2167849560 · doi:10.1002/hon.703

Increasing incidence of non‐Hodgkin's lymphoma in Canada, 1970–1996: age–period–cohort analysis

2003· article· en· W2167849560 on OpenAlexaffabout
Shiliang Liu, R Semenciw, Yang Mao

Bibliographic record

VenueHematological Oncology · 2003
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsInstitute of Population and Public HealthHealth CanadaUniversity of Ottawa
Fundersnot available
KeywordsIncidence (geometry)MedicineCohortLymphomaNon-Hodgkin's lymphomaCohort effectCancer registryDemographyHodgkin lymphomaCohort studyCancerPediatricsInternal medicine

Abstract

fetched live from OpenAlex

Previous studies have shown that the incidence of non-Hodgkin's lymphoma (NHL) has increased in many parts of the world in recent decades. Using data obtained from the Canadian Cancer Registry, the present study examined time trends in NHL incidence in Canada between 1970 and 1996 and the effects of age, period of diagnosis and birth cohort on incidence patterns for each sex separately. Results showed that overall age-adjusted incidence rates increased substantially, from 7.3 and 5.2 per 100,000 in 1970-1971 to 14.0 and 10.0 per 100,000 in 1995-1996 in males and females, respectively. Diffuse lymphoma was the major histological subtype, accounting for approximately 76% of NHL cases over the 27-year period. The data suggest that period effects have played a major role, although birth cohort effects may also have been involved. Sex-specific patterns of the incidence were similar over the time period of diagnosis but were distinct among recent birth cohorts. In conclusion, there is in fact a marked increase in NHL in Canada which cannot be explained in terms of improvements in diagnosis, changes in NHL classification and the increase in AIDS-associated NHL alone. The birth cohort effect in NHL suggests that changes in risk factors may have contributed to the observed increase.

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)0.001
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 score0.097
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.292
Teacher spread0.275 · 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

Citations55
Published2003
Admission routes2
Has abstractyes

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