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Mortality among five-year survivors of childhood cancer: Results over five decades of follow-up in the Childhood Cancer Survivor Study.

2021· article· en· W3170865790 on OpenAlexaff
Stephanie B. Dixon, Qi Liu, Matthew J. Ehrhardt, Eric J. Chow, Kevin C. Oeffinger, Ann C. Mertens, Paul C. Nathan, Rebecca M. Howell, Wendy M. Leisenring, Kevin R. Krull, Kirsten K. Ness, Melissa M. Hudson, Leslie L. Robison, Yutaka Yasui, Gregory T. Armstrong

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick ChildrenUniversity of Alberta
FundersNational Institutes of Health
KeywordsMedicineCancerMortality rateConfidence intervalPediatricsNational Death IndexPopulationCause of deathDemographyStandardized mortality ratioInternal medicineHazard ratioDiseaseEnvironmental health

Abstract

fetched live from OpenAlex

10013 Background: Adult survivors of childhood cancer are at greater risk for late mortality compared to the general population due to cancer and its treatment. Risk factors, patterns and specific causes of late mortality across the lifespan are not well established. Methods: All-cause, cause-specific, and health-related late mortality (HRM; excludes death from primary cancer and external causes) > 5 years from diagnosis were evaluated in survivors diagnosed < 21 years of age between 1970-1999. Cause of death was based on ICD codes from the National Death Index through December 2017. Cumulative mortality, mortality rates and standardized mortality ratios (SMRs) with 95% confidence intervals (CIs) were estimated, overall and in 5- and 10-year survival periods. Results: Among 34,230 survivors (median time from diagnosis 29.1 years, range 5.0 - 48.0) the 40-year cumulative mortality was 23.3% (95% CI 22.7 - 24.0). Of 5,916 deaths, 3,061 (51.2%) were attributable to health-related causes including subsequent neoplasm (n = 1,458), cardiac (n = 504), and pulmonary causes (n = 238). All-cause mortality by time from diagnosis demonstrated a U-shaped distribution: 10.1 deaths/1000 person-years at 5-9 years, largely due to recurrence of the primary cancer, decreasing to 4.1 at 15-19 years before increasing to 18.5 at 40-48 years, attributable to an increasing mortality rate from HRM (2.3 at 5-9 years; 17.0 at 40-48 years). For the interval 5-9 years from diagnosis, survivors had an 18.1-fold (95% CI 17.3-18.9) higher risk of death from any cause, and a 13.1-fold (11.9-13.4) higher risk for HRM when compared to the general population. Although the SMRs declined with duration of follow-up, survivors had a 4-fold higher risk of death overall, attributable to a more than 4-fold increased risk of HRM. HRM 40-48 years from diagnosis was largely attributable to an increased risk of death due to subsequent neoplasm (SMR 6.0, 95% CI 4.9-7.2), cardiac (3.9, 2.9-5.0) and pulmonary (5.6, 3.6-8.4) causes. Cause-specific mortality remained markedly elevated at 40-48 years from diagnosis: CNS malignancy (SMR 11.7, 95% CI 5.4-22.3), benign meningioma (171.3, 34.4-500.5), valvular heart disease (39.8, 21.2-68.1), cardiomyopathy (10.4, 4.5-20.5), stroke (7.9, 4.6-12.6), and renal failure (5.6, 1.8-13.2). HRM was significantly higher among the youngest group of survivors (0-4 years at diagnosis), non-Hispanic blacks and those who received radiation to the brain, chest or total body, or who were exposed to anthracycline, alkylating or platinum chemotherapy. Conclusions: After five decades, aging survivors consistently remain at higher risk of all-cause mortality compared to the general, aging population, primarily due to a persistent 4-fold increased risk of HRM. Continued late-effects surveillance and reduction of therapies associated with long-term morbidity and increased mortality is essential.

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)0.002
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
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 head0.102
GPT teacher head0.470
Teacher spread0.368 · 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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Citations1
Published2021
Admission routes1
Has abstractyes

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