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Mortality and the burden of subsequent malignant neoplasms in survivors of childhood cancer beyond age 50: A report from the Childhood Cancer Survivor Study (CCSS).

2023· article· en· W4379338732 on OpenAlexaff
Rusha Bhandari, Yan Chen, Eric J. Chow, Rebecca M. Howell, Lisa B. Kenney, Kevin R. Krull, Wendy M. Leisenring, Paul C. Nathan, Joseph Philip Neglia, Kirsten K. Ness, Kevin C. Oeffinger, Claire Snyder, Lucie M. Turcotte, F. Lennie Wong, Yutaka Yasui, Gregory T. Armstrong, Saro H. Armenian

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick ChildrenUniversity of Alberta
FundersNational Institutes of Health
KeywordsMedicineCancerConfidence intervalPopulationIncidence (geometry)Cumulative incidenceDemographyStandardized mortality ratioMortality ratePediatricsInternal medicineCohortEnvironmental health

Abstract

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10052 Background: The first generation of survivors of childhood cancer is now older than 50 years (y) of age. There is a paucity of information on the risk for late mortality and the evolving burden of subsequent malignant neoplasms (SMNs) in these aging survivors. Methods: We assessed cause-specific mortality (National Death Index) and self-reported SMNs (excluding nonmelanoma skin cancers) among CCSS participants diagnosed between 1970-1999, conditional on having survived to 50y by 12/31/2017. There were 4,772 survivors alive at 50y eligible for mortality analyses. Of the 3,355 who completed a baseline CCSS questionnaire, 2,273 also completed a follow-up questionnaire at ≥50y and were included in the SMN analyses. Cumulative mortality, standardized mortality ratios (SMRs), and, for SMNs, cumulative burden, standardized incidence ratios (SIRs), and relative rate (RR) with 95% confidence intervals (CIs) were calculated, compared with the general US population and by survivor subgroups. Piecewise-exponential multivariable regression was used to identify risk factors associated with the development of SMNs after 50y. Results: Mean age at diagnosis was 14y (standard deviation 4.2y). Mortality: Among survivors who attained 50y of age, the subsequent 5y, 10y, and 15y incidences of all-cause mortality were 9%, 19%, and 35%, respectively, (overall SMR 3.7 [95% CI 3.4-4.1]). SMRs were highest for SMN (SMR 5.1 [95% CI 4.4-5.9]), pulmonary (SMR 4.7 [95% CI 3.3-6.5]) and cardiovascular (SMR 3.9 [95% CI 3.2-4.8]) causes of death. The highest SMR (SMR 9.2 [95% CI 7.7-10.8]) was seen in female survivors of Hodgkin lymphoma. SMN: By age 50y, 14% of CCSS participants reported >1 SMN. History of SMN <50y was independently associated with the rate of developing another SMN at >50y (RR 1.6 [95% CI 1.1-2.3]). The most frequent SMNs ≥50y were breast (41%) in females and prostate (21%) in males, followed by gastrointestinal cancers in both (14% [females], 19% [males]). The cumulative burden of SMNs in survivors treated with radiation therapy (RT) increased sharply with age and exceeded 50 per 100 survivors by 65y (SIR 3.3 95%CI 2.0-5.2]). Survivors who did not receive RT had a SMN rate comparable to the general population (Table). Conclusions: This sentinel population of survivors of childhood cancer is at high risk for poor outcomes as they enter older adulthood. RT exposure and history of SMN <50y are associated with increased risk for developing SMNs after 50y. The evolving burden of late morbidity attributed to SMNs may be lower for contemporary survivors projected to have less RT exposure. [Table: see text]

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)0.001
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.129
GPT teacher head0.461
Teacher spread0.332 · 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
Published2023
Admission routes1
Has abstractyes

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