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Record W2254403995 · doi:10.1089/thy.2015.0319

Anthropometric Factors and Thyroid Cancer Risk by Histological Subtype: Pooled Analysis of 22 Prospective Studies

2016· article· en· W2254403995 on OpenAlexaff
Cari M. Kitahara, Marjorie L. McCullough, Silvia Franceschi, Sabina Rinaldi, Alicja Wolk, Gila Neta, Hans Olov Adami, Kristin E. Anderson, Gabriella Andreotti, Laura E. Beane Freeman, Leslie Bernstein, Julie E. Buring, Françoise Clavel‐Chapelon, Lisa A. De Roo, Yu-Tang Gao, J. Michael Gaziano, Graham G. Giles, Niclas Håkansson, Pamela L. Horn‐Ross, Vicki A. Kirsh, Martha S. Linet, Robert J. MacInnis, Nicola Orsini, Yikyung Park, Alpa V. Patel, Mark P. Purdue, Elio Ríboli, Kim Robien, Thomas E. Rohan, Dale P. Sandler, Catherine Schairer, Arthur B. Schneider, Howard D. Sesso, Xiao‐Ou Shu, Pramil N. Singh, Piet A. van den Brandt, Elizabeth Ward, Elisabete Weiderpass, Emily White, Yong-Bing Xiang, Anne Zeleniuch‐Jacquotte, Wei Zheng, Patricia Hartge, Amy Berrington de González

Bibliographic record

VenueThyroid · 2016
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsCancer Care OntarioOntario Institute for Cancer ResearchPublic Health OntarioUniversity of Toronto
FundersNational Institute of Environmental Health SciencesNational Center for Chronic Disease Prevention and Health PromotionNational Heart, Lung, and Blood InstituteWellcome TrustBritish Heart FoundationNational Cancer InstituteCancer Research UKWorld Health Organization
KeywordsMedicineThyroid cancerHazard ratioBody mass indexWaistInternal medicineProspective cohort studyAnthropometryProportional hazards modelCancerThyroidIncidence (geometry)OncologyEndocrinologyConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Greater height and body mass index (BMI) have been associated with an increased risk of thyroid cancer, particularly papillary carcinoma, the most common and least aggressive subtype. Few studies have evaluated these associations in relation to other, more aggressive histologic types or thyroid cancer-specific mortality. METHODS: This large pooled analysis of 22 prospective studies (833,176 men and 1,260,871 women) investigated thyroid cancer incidence associated with greater height, BMI at baseline and young adulthood, and adulthood BMI gain (difference between young-adult and baseline BMI), overall and separately by sex and histological subtype using multivariable Cox proportional hazards regression models. Associations with thyroid cancer mortality were investigated in a subset of cohorts (578,922 men and 774,373 women) that contributed cause of death information. RESULTS: During follow-up, 2996 incident thyroid cancers and 104 thyroid cancer deaths were identified. All anthropometric factors were positively associated with thyroid cancer incidence: hazard ratios (HR) [confidence intervals (CIs)] for height (per 5 cm) = 1.07 [1.04-1.10], BMI (per 5 kg/m2) = 1.06 [1.02-1.10], waist circumference (per 5 cm) = 1.03 [1.01-1.05], young-adult BMI (per 5 kg/m2) = 1.13 [1.02-1.25], and adulthood BMI gain (per 5 kg/m2) = 1.07 [1.00-1.15]. Associations for baseline BMI and waist circumference were attenuated after mutual adjustment. Baseline BMI was more strongly associated with risk in men compared with women (p = 0.04). Positive associations were observed for papillary, follicular, and anaplastic, but not medullary, thyroid carcinomas. Similar, but stronger, associations were observed for thyroid cancer mortality. CONCLUSION: The results suggest that greater height and excess adiposity throughout adulthood are associated with higher incidence of most major types of thyroid cancer, including the least common but most aggressive form, anaplastic carcinoma, and higher thyroid cancer mortality. Potential underlying biological mechanisms should be explored in future studies.

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)0.011
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.012
Bibliometrics0.0050.007
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.022
GPT teacher head0.316
Teacher spread0.294 · 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.

Study designMeta-analysis
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

Citations194
Published2016
Admission routes1
Has abstractyes

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