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Record W4405050202 · doi:10.1182/blood-2024-209461

Population-Based Analysis of Outcomes in Acute Myeloid Leukemia from a Provincial Cancer Registry and the Canadian Cancer Registry: A Population-Based Retrospective Cohort Study

2024· article· en· W4405050202 on OpenAlexaffabout
Yaqeen Abduallah, Neelan Sriranjan, Kristjan Paulson, Brett L. Houston

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of British ColumbiaCancerCare Manitoba
Fundersnot available
KeywordsCancer registryMedicineMyeloid leukemiaCancerRetrospective cohort studyPopulationInternal medicineCohortOncologyDemographyEnvironmental health

Abstract

fetched live from OpenAlex

Background: Acute myeloid leukemia (AML) is the most common acute leukemia in adults, accounting for approximately 80 % of cases in adults. The age-standardized incidence rate in Canada is 3.46 cases of AML per 100,000 person-years. Despite advances in therapeutic modalities, the prognosis of AML remains guarded. The landscape of AML management has changed in recent years, due to advancements in molecular diagnostics, targeted therapies, and transplantation. A comprehensive understanding of its epidemiology and outcomes is necessary for informed clinical decision-making. Methods: This retrospective cohort study aimed to comprehensively assess the outcomes and examine two-year relative survival in individuals diagnosed with AML between 1990 and 2014 in Manitoba and between 1995 and 2014 in Canada. Two-year relative survival was chosen as the outcome of interest because it describes survival during the transition away from direct cancer care back to community care and follow-up. This provides an estimate of the effect of AML on mortality by removing the impact of other causes of death. We analysed the Canadian Cancer Registry (CCR), which contains data on all Canadians with AML. To provide clinical information lacking in the CCR, we obtained data from the Manitoba Cancer Registry (MCR). Data from the MCR added cytogenetic data and therapeutic implications such as the use of azacitidine and receipt of a bone marrow transplant. Comprehensive analyses revealed key demographic trends, such as age distribution and gender disparities. Results: There were 18,975 individuals included in the analysis with all of Canada and 785 individuals included in the Manitoba-only analysis. Baseline demographics of the Canadian cohort and Manitoba cohorts were similar, with few exceptions. In both cohorts, approximately 55 % of each cohort were male. The percent of individuals in the cohort increased with age at diagnosis. In Canada, 81 % of individuals lived in urban regions, whereas only 71 % of individuals in Manitoba lived in urban regions. Overall, there was a 2% decrease in excess mortality for each increase in diagnosis year in both cohorts studied. This demonstrates a steady improvement in AML outcomes over time and highlights our advancements in care for patients with AML over the years. Additionally, in the Canadian cohort, there was a 9 % decrease in excess mortality for individuals who live in urban regions. This improved survival in patients in an urban setting as compared to those in a rural setting may be attributed to increased access to cancer treatment facilities and emergency resources in urban regions. Conversely, when looking at individuals diagnosed in Manitoba only, there was no evidence of a difference in excess mortality for rurality. The data from the provincial registry expectedly demonstrated a 48 % decrease in excess mortality for individuals who received a bone marrow transplant. Advances in supportive care measures have importantly reduced mortality rates after transplantation. There was no evidence of a significant difference in excess mortality with azacitidine use. Conclusion: There is a steady improvement in AML survival rates over the study period, reflecting advancements in therapy and supportive care. Notably, there is improved survival rates in urban patients compared to their rural counterparts, highlighting the importance of addressing geographical differences in healthcare access and equity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.011
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.302
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes2
Has abstractyes

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