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Increased Risk of All Cardiovascular Disease Subtypes Among Childhood Cancer Survivors

2019· article· en· W2973731360 on OpenAlexafffund
Ashna Khanna, Priscila Pequeno, Sumit Gupta, Paaladinesh Thavendiranathan, Douglas S. Lee, Husam Abdel‐Qadir, Paul C. Nathan

Bibliographic record

VenueCirculation · 2019
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsWomen's College HospitalTed Rogers Centre for Heart ResearchPediatric Oncology GroupHospital for Sick Children
FundersHospital for Sick ChildrenOntario Ministry of Health and Long-Term Care
KeywordsMedicineDiseaseChildhood cancerCancerInternal medicine

Abstract

fetched live from OpenAlex

anthracyclines ◼ chemotherapy ◼ diabetes mellitus ◼ heart failure ◼ hypertension ◼ survivors of childhood cancer C hildhood cancer survivors are at risk for a range of cardiovascular diseases (CVD) as a consequence of their cancer therapy. 1 However, most studies have focused on anthracycline-related heart failure (HF) only.To address these gaps, we evaluated the risks and predictors of HF, arrhythmias, pericardial disease, valvular disease, and coronary artery disease in a population-based cohort of childhood cancer survivors by leveraging health administrative data from Canada's largest province, Ontario.We used the provincial pediatric cancer registry, Pediatric Oncology Group of Ontario Networked Information System, to identify all 5-year cancer survivors diagnosed before age 18 years who were treated in a pediatric cancer center between 1987 and 2010.The registry provided detailed demographic, diagnosis, and treatment data.Each survivor was matched to 5 cancer-free individuals from the general population based on age, sex, and postal code.The index date was defined as 5 years from the survivor's last pediatric cancer diagnosis.All provincial residents receive coverage for medically necessary services.ICES, a nonprofit research institute that holds an array of Ontario's health-related data, maintains various population-based health administrative databases that are linkable through encrypted, individual health card numbers.We identified cardiac events, diabetes, and hypertension using established algorithms based on combinations of hospital admission and physician billing codes.We used cumulative incidence estimates and cause-specific hazards ratios (HRs) to compare the risk of CVD between cohorts, accounting for matching and competing risks (other CVD subtypes, noncardiac death).Multivariable analyses, using proportional hazards models that accounted for clustering due to matching, were used to examine the association between baseline characteristics and cancer treatments with CVD rates among survivors.All predictors were checked for violations of model assumptions.The study was approved by the Research Ethics Boards of The Hospital for Sick Children and Sunnybrook Health Sciences Centre.We studied 7289 5-year survivors (median age at diagnosis, 7 years; range, 0-17.9 years) and 36 205 matched cancer-free individuals with a combined median attained age of 24 years (range, 5-47 years) at the end of follow-up.Over a median follow-up of 10 years from index (range, 0-25 years), 203 survivors (2.8%) experienced one or more cardiac events compared with 331 (0.9%) controls (P<0.001).Survivors experienced 3.2 cardiac events per 1000 person-years (95% CI, 2.8-3.6)compared with 0.9 cardiac events per 1000 person-years in the general population (CI, 0.9-1.9).The Table presents the cumulative incidence and cause-specific HRs for each CVD subtype.Cause-specific HRs were significantly elevated in survivors compared with the general population for all CVD subtypes.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.260
Teacher spread0.244 · 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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Citations57
Published2019
Admission routes2
Has abstractno

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