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Record W2988645140 · doi:10.1182/blood-2019-130145

Outcome Among Adolescents and Young Adults with Acute Myeloid Leukemia at Pediatric Versus Adult Centers: A Population-Based Study Using the IMPACT Cohort

2019· article· en· W2988645140 on OpenAlexaffabout
Sumit Gupta, Nancy N. Baxter, Jason D. Pole, Cindy Lau, Rinku Sutradhar, Chenthila Nagamuthu, Paul C. Nathan

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick ChildrenOccupational Cancer Research CentreUniversity of TorontoPediatric Oncology GroupSt. Michael's HospitalInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsMedicineHazard ratioPopulationCohortYoung adultProportional hazards modelMyeloid leukemiaInternal medicinePediatricsCancerSurvival analysisOncologyConfidence interval

Abstract

fetched live from OpenAlex

Background: Survival outcomes among adolescents and young adults (AYA) with acute myeloid leukemia (AML) remain poor. In AYA with acute lymphoblastic leukemia, outcomes differ between patients treated in pediatric vs. adult centers. This has not been well evaluated in AML. We therefore compared outcomes between AYA with AML treated at pediatric vs. adult centers using a population-based clinical database. In addition, we determined other predictors of outcome within this population. Methods: The IMPACT Cohort comprises all Ontario, Canada AYA aged 15-21 years diagnosed with one of six common cancers (including AML) between 1992-2012. Detailed demographic, disease, treatment, and outcome data were collected through chart abstraction and validated by content experts. Locus of cancer care (LOC - pediatric vs. adult center) was determined based on where the majority of therapy was delivered in the first three months after diagnosis. Linkage to population-based health administrative data identified additional cancer events (second cancers, relapse, death). Event-free (EFS) and overall survival (OS) were determined using Kaplan-Meier methods. The impact of LOC on EFS and OS was determined using multivariable Cox proportional hazard models, adjusting for demographic, disease, and treatment variables. Events included disease progression, relapse, death, and second malignancies. Results: Among 140 AYA with AML, 89 (63.6%) received therapy at an adult center. AYA treated in pediatric centers were younger than those treated at adult centers (median 16 years vs. 19 years; p<0.001) and were more likely to live in higher-income neighborhoods [37/51 (72.5%) vs. 47/89 (52.8%); p=0.02]. Disease markers such as presenting white blood cell count and AML subtype did not differ by LOC. The 5-year EFS and OS for the whole cohort were 35.0%±4.0% and 53.6%±4.2%. Neither EFS nor OS differed by LOC (Table 1). In multivariable analyses adjusting for disease characteristics, LOC was not predictive of either EFS [adult vs. pediatric center hazard ratio (HR) 1.3, 95thconfidence interval (CI) 0.8-2.2, p=0.27] or OS (HR 1.0, CI 0.6-1.6, p=0.97). AYA with AML living in rural areas however experienced significantly inferior outcomes as compared to their urban counterparts (EFS: HR 2.5, CI 1.3-4.7, p=0.005; OS: HR 2.0, CI 1.1-3.8, p=0.04). Conclusions: In this population-based cohort, outcomes did not differ between AYA with AML treated at pediatric vs. adult centers, unlike what has been previously shown in AYA with acute lymphoblastic leukemia. However, rural AYA experienced substantially inferior outcomes than urban AYA, suggesting that even within a universal single payer system of healthcare, socioeconomic disparities persist in this population. Disclosures No relevant conflicts of interest to declare.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.163
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.294
Teacher spread0.279 · 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".

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Citations2
Published2019
Admission routes2
Has abstractyes

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