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Record W2464466306 · doi:10.2188/jea.je20150268

The Tohoku Medical Megabank Project: Design and Mission

2016· article· en· W2464466306 on OpenAlexaff
Shinichi Kuriyama, Nobuo Yaegashi, Fuji Nagami, Tomohiko Arai, Yoshio Kawaguchi, Noriko Osumi, Masaki Sakaida, Yoichi Suzuki, Keiko Nakayama, Hiroaki Hashizume, Gen Tamiya, Hiroshi Kawame, Kichiya Suzuki, Atsushi Hozawa, Naoki Nakaya, Masahiro Kikuya, Hirohito Metoki, Ichiro Tsuji, Nobuo Fuse, Hideyasu Kiyomoto, Junichi Sugawara, Akito Tsuboi, Shinichi Egawa, Kiyoshi Ito, Koichi Chida, Tadashi Ishii, Hiroaki Tomita, Yasuyuki Taki, Naoko Minegishi, Naoto Ishii, Jun Yasuda, Kazuhiko Igarashi, Ritsuko Shimizu, Masao Nagasaki, S. Koshiba, Kengo Kinoshita, Soichi Ogishima, Takako Takai‐Igarashi, Teiji Tominaga, Osamu Tanabe, Noriaki Ohuchi, Toru Shimosegawa, Shigeo Kure, Hiroshi Tanaka, Sadayoshi Ito, Jiro Hitomi, Kozo Tanno, Motoyuki Nakamura, Kuniaki Ogasawara, Seiichiro Kobayashi, Kiyomi Sakata, Mamoru Satoh, Atsushi Shimizu, Makoto Sasaki, Ryujin Endo, Kenji Sobue

Bibliographic record

VenueJournal of Epidemiology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsInstitute of Aging
FundersMinistry of Education, Culture, Sports, Science and TechnologyJapan Agency for Medical Research and Development
KeywordsBiobankMedicineCohortCohort studyProspective cohort studyPopulationEnvironmental healthFamily medicinePediatricsSurgeryPathology

Abstract

fetched live from OpenAlex

The Great East Japan Earthquake (GEJE) and resulting tsunami of March 11, 2011 gave rise to devastating damage on the Pacific coast of the Tohoku region. The Tohoku Medical Megabank Project (TMM), which is being conducted by Tohoku University Tohoku Medical Megabank Organization (ToMMo) and Iwate Medical University Iwate Tohoku Medical Megabank Organization (IMM), has been launched to realize creative reconstruction and to solve medical problems in the aftermath of this disaster. We started two prospective cohort studies in Miyagi and Iwate Prefectures: a population-based adult cohort study, the TMM Community-Based Cohort Study (TMM CommCohort Study), which will recruit 80 000 participants, and a birth and three-generation cohort study, the TMM Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study), which will recruit 70 000 participants, including fetuses and their parents, siblings, grandparents, and extended family members. The TMM CommCohort Study will recruit participants from 2013 to 2016 and follow them for at least 5 years. The TMM BirThree Cohort Study will recruit participants from 2013 to 2017 and follow them for at least 4 years. For children, the ToMMo Child Health Study, which adopted a cross-sectional design, was also started in November 2012 in Miyagi Prefecture. An integrated biobank will be constructed based on the two prospective cohort studies, and ToMMo and IMM will investigate the chronic medical impacts of the GEJE. The integrated biobank of TMM consists of health and clinical information, biospecimens, and genome and omics data. The biobank aims to establish a firm basis for personalized healthcare and medicine, mainly for diseases aggravated by the GEJE in the two prefectures. Biospecimens and related information in the biobank will be distributed to the research community. TMM itself will also undertake genomic and omics research. The aims of the genomic studies are: 1) to construct an integrated biobank; 2) to return genomic research results to the participants of the cohort studies, which will lead to the implementation of personalized healthcare and medicine in the affected areas in the near future; and 3) to contribute the development of personalized healthcare and medicine worldwide. Through the activities of TMM, we will clarify how to approach prolonged healthcare problems in areas damaged by large-scale disasters and how useful genomic information is for disease prevention.

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)0.020
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.013
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0350.008

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 head0.077
GPT teacher head0.362
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations380
Published2016
Admission routes1
Has abstractyes

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