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Record W2561293383 · doi:10.1158/1538-7445.am2015-3713

Abstract 3713: Second malignant neoplasms after non-CNS embryonal tumors in North America

2015· article· en· W2561293383 on OpenAlexaffabout
Xuchen Zong, Jason D. Pole, Paul E. Grundy, Salaheddin M. Mahmud, Louise Parker

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsPediatric Oncology GroupAtlantic School of TheologyLunenfeld-Tanenbaum Research InstituteUniversity of ManitobaMount Sinai Hospital
Fundersnot available
KeywordsMedicineCancerEpidemiologyCancer registryPopulationInternal medicineIncidence (geometry)OncologyBone cancerLeukemia

Abstract

fetched live from OpenAlex

Abstract Background: Childhood cancer survivors are known to face an increased risk of adverse medical conditions related to the initial disease or cancer treatment; however, few studies have quantified the risk of second malignant neoplasms (SMNs) among survivors of childhood non-CNS embryonal cancer due to its rarity. In this study, we combined data from the U.S. and Canada to investigate the risk of SMNs. Methods: Data from 13,107 survivors of childhood non-CNS embryonal tumors reported to the Surveillance Epidemiology and End Results (SEER) program in the U.S. and 8 population-based cancer registries in Canada (between 1973 to 2010) were analyzed. We calculated standardized incidence ratios (SIRs) for all types of second primaries by type of first primary, age at first cancer diagnosis, and follow-up duration. Expected numbers of SMNs were obtained by applying age-, sex-, calendar year-, and registry-specific cancer incidence rates to person-years contributed by these cancer survivors. Results: One hundred and ninety SMNs were reported over 134,548 person-years of follow-up, with bone and joint cancer and leukemia being the most common SMNs (19% and 16% respectively). The SIR for all SMNs combined was 6.4 (95% CI: 5.53-7.35). Most site-specific SIRs were significantly increased, ranging from 36.4 (95% CI: 25.5-49.2) for bone and joint cancer, to 3.1 (95% CI: 1.54-5.2) for brain tumor. Among all the survivors, those who survived rhabdomyosarcoma carried the greatest risk of SMN development with SIR of 11.5 (95% CI: 8.7-14.6). Bone and joint tumors were mostly seen among retinoblastoma survivors, reflecting a markedly risk of 139.3 (95% CI: 85-207). When stratified by follow-up period, we observed that the risk patterns varied by SMN site. For example, the risk for a second malignancy in the thyroid remained significantly increased even after 20 years of follow-up, whereas the elevated risk of secondary leukemia persisted for 10 years but decreased rapidly afterwards. Details of stratified analyses by first primary tumor site, age at first diagnosis, and follow-up duration will be presented. Conclusion: Survivors of childhood non-CNS embryonal tumors have increased risks of developing SMNs compared to the general population. Increased surveillance may explain the elevated risks within the first few years of the initial diagnosis, but treatment effects and shared etiology most likely account for the excess risk later in life. Citation Format: Xuchen Zong, Jason D. Pole, Paul Grundy, Salaheddin M. Mahmud, Louise Parker, Rayjean J. Hung. Second malignant neoplasms after non-CNS embryonal tumors in North America. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3713. doi:10.1158/1538-7445.AM2015-3713

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 score0.338
Threshold uncertainty score0.672

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.084
GPT teacher head0.399
Teacher spread0.315 · 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
Published2015
Admission routes2
Has abstractyes

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