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Record W2741998413 · doi:10.1186/s12889-017-4644-6

The incidence of acute myeloid leukemia in Calgary, Alberta, Canada: a retrospective cohort study

2017· article· en· W2741998413 on OpenAlexafffundabout
Andrea Christine Shysh, Leonard T. Nguyen, Maggie Guo, Marcus Vaska, Christopher Naugler, Fariborz Rashid-Kolvear

Bibliographic record

VenueBMC Public Health · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsAlberta Health ServicesCalgary Laboratory ServicesUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicineIncidence (geometry)Myeloid leukemiaEpidemiologyDemographyPopulationRetrospective cohort studyBiostatisticsPediatricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: The incidence rate of acute myeloid leukemia (AML) was determined in the Calgary Metropolitan Area, a major Canadian city. METHODS: Data from all patients diagnosed with AML between January 1, 2011 and December 31, 2015 were retrieved from a single, centralized cancer cytogenetics laboratory for bone marrow samples, the sole diagnostic facility of its kind in Southern Alberta. RESULTS: The calculated incidence rate was 2.79 cases per 100,000 person-years with a median age of 60, slightly lower than previously published data. The age-standardized incidence rate for Canada was 3.46 cases per 100,000 person-years. The higher value is reflective of Calgary's younger population compared to the rest of Canada. Higher male incidence and greatest incidence occurring at approximately the age of 85 is similar to data from other developed countries. The lower incidence rates and median age of diagnosis, in comparison with that of other high-income nations, may be due to differences in the proportion of aging citizens in the population. CONCLUSION: This is the first published incidence rate of acute myeloid leukemia (AML) in Canada across all age groups.

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)0.005
metaresearch head score (Gemma)0.004
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 score0.052
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.335
Teacher spread0.307 · 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

Citations72
Published2017
Admission routes3
Has abstractyes

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