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Record W2083131397 · doi:10.1155/2012/103032

Risk of Late Mortality and Second Malignant Neoplasms among 5-Year Survivors of Young Adult Cancer: A Report of the Childhood, Adolescent, and Young Adult Cancer Survivors Research Program

2012· article· en· W2083131397 on OpenAlexafffund
Yang Zhang, Karen Goddard, John J. Spinelli, Carolyn Gotay, Mary L. McBride

Bibliographic record

VenueJournal of Cancer Epidemiology · 2012
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersBC Cancer AgencyCanadian Cancer Society Research Institute
KeywordsMedicineCancerYoung adultIncidence (geometry)Absolute risk reductionCancer registryPopulationProportional hazards modelChildhood cancerDemographyDiseaseCause of deathPediatricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

We conducted a population-based retrospective study to assess the long-term risks of overall and cause-specific mortality and second malignant neoplasm (SMN) among survivors of young adult cancer compared to the risk in British Columbia (BC) population and to evaluate the effects of demographic and clinical factors on risk. 1248 5-year survivors of young adult cancer diagnosed 1970-1995 between 20 and 24 years of age were identified from the BC Cancer Registry and followed to the end of 2007. Standardized mortality ratios (SMRs) and standardized incidence ratios (SIRs) were calculated. The Cox proportional hazards model was used to estimate the effects of different demographic and disease-related characteristics on the risk of death and SMN. A total of 138 deaths and 62 SMNs were observed during follow-up. The overall SMR was 5.9 (95% CI 4.9-6.9) and the absolute excess risk was 5.3 per 1,000 person-years. The overall SIR was 3.0 (95% CI 2.3-3.8). Treatment with radiation resulted in increased risks of death and SMN. These observed increased risks emphasize the importance of prevention, surveillance, and treatment of late effects in survivors of young adult cancers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.408
Teacher spread0.345 · 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 teacher head, not a consensus.

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

Citations40
Published2012
Admission routes2
Has abstractyes

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