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Epidemiology of ophthalmic lymphoma in Canada during 1992–2010

2019· article· en· W2987801113 on OpenAlexafffundabout
Rami Darwich, Feras M. Ghazawi, Elham Rahme, Nebras Alghazawi, Andrei Zubarev, Linda Moreau, Denis Sasseville, Miguel N. Burnier, Ivan V. Litvinov

Bibliographic record

VenueBritish Journal of Ophthalmology · 2019
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsMcGill University Health CentreOttawa HospitalMcGill UniversityUniversity of OttawaDalhousie University
FundersCanadian Dermatology Foundation
KeywordsMedicineEpidemiologyIncidence (geometry)DemographyCancer registryPopulationFollicular lymphomaLymphomaCancerEnvironmental healthPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Ophthalmic lymphoma (OL) is the most common orbital tumour, particularly in older individuals. Little is known about the epidemiology and geographic distribution of OL in Canada. Descriptive demographic statistics are an important first step in understanding OL burden and are necessary to inform comprehensive national cancer prevention programmes. METHODS: We determined patterns of incidence and geographical distribution of the three major subtypes of OL: extranodal marginal zone B cell lymphoma, follicular lymphoma (FL) and diffuse large B cell lymphoma. Here, we used cases that were diagnosed during 1992-2010 using two independent population-based cancer registries, the Canadian Cancer Registry and Le Registre Québécois du Cancer (LRQC). RESULTS: The OL mean annual age-standardised incidence rate for 1992-2010 was 0.65 cases per million people per year with an average annual increase in the incidence rate of 4.5% per year. The mean age of diagnosis was 65 years. OL incidence rate was the highest in the cities located along the heavily industrialised Strait of Georgia in British Columbia. CONCLUSIONS: Our data on patient age, sex and temporal trends showed similarities with data reported in the USA and Denmark. Additional studies are needed to determine whether the observed increase in OL incidence is genuine or spurious.

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 score0.595
Threshold uncertainty score0.989

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)0.0010.000

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 head0.021
GPT teacher head0.270
Teacher spread0.249 · 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

Citations19
Published2019
Admission routes3
Has abstractyes

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