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Record W2077104741 · doi:10.14740/jocmr2097w

Prevalence of Human T-Cell Leukemia Virus Type 1 Carrier in Japanese Pregnant Women in 2013

2015· article· en· W2077104741 on OpenAlexvenueno aff
Shunji Suzuki, Masanobu Tanaka, Hideo Matsuda, Yuki Tsukahara, Yasushi Kuribayashi, Akihito Nakai, Ryoichiro Miyazaki, Naoki Kamiya, Akihiko Sekizawa, Nobuko Mizutani, Katsuyuki Kinoshita

Bibliographic record

VenueJournal of Clinical Medicine Research · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLeukemiaVirologyVirusPregnancyImmunologyGeneticsBiology

Abstract

fetched live from OpenAlex

Since September 2010, in Japan serological screening for the detection of human T-cell leukemia virus type 1 (HTLV-1) antibodies can be performed for all women during pregnancy with the Japanese public funds for strategies for prevention of HTLV-1 vertical transmission, because Japan, especially Kyushu area, has been reported to be one of the areas of highest prevalence of HTLV-1 in the world [1, 2]. In our previous study [3], we examined the prevalence of HTLV-1 carrier in Japanese pregnant women according to the implement rate and results of HTLV-1 screening and confirmation tests of women who gave births in Japan in 2011. The total rates of positive HTLV-1 screening tests and positive western blot (WB) test in positive screening tests were 0.32% and 49.8%, respectively in 2011. Considering the response rate and the rate of implementation of WB test, the number of HTLV-1 carrier in Japanese pregnant women in 2011 was estimated to be 1,560 (0.15%). In addition, although the number of delivery in Kyushu area was only 14% of Japanese deliveries, 53% of HTLV-1 carrier of Japanese pregnant women was present in Kyushu area. Recently, the migration of Japanese people from Kyushu area to the metropolitan areas has been thought to contribute to a significant decrease of HTLV-1 carriers in Kyushu area and an increase in Kanto (including Tokyo) area in Japan [1, 4]. To confirm this migration in Japanese pregnant women, on December 2014, we requested again 2,544 obstetrical facilities that are members of Japan Association of Obstetricians and Gynecologists (JAOG) to provide information of HTLV-1 tests in pregnant women who delivered at ≥ 22 weeks’ gestation in 2013. A total of 1,356 (53.3%) of 2,544 obstetrical facilities responded and information on a total of 538,167 women, accounting for approximately 54% of all deliveries that occurred in Japan during the study period (approximately 1,001,800 births) was provided. In 2013, the total rates of positive HTLV-1 screening tests and positive WB test in positive screening tests were 0.35% and 50.8%, respectively. Considering the response rate and the rate of implementation of WB test, the number of HTLV-1 carrier in Japanese pregnant women in 2013 was estimated to be 1,780 (0.18%). Table 1 shows the difference in the estimated number of HTLV-1 carrier based on positive WB test by area in Japan between 2011 and 2013. The estimated number of HTLV-1 carrier in 2013 seemed to be more than that in 2011, especially in the northeast and southwest (Kyushu) areas. In addition, 51% of HTLV-1 carrier of Japanese pregnant women was present in Kyushu area, although the number of delivery in Kyushu area was only 13% of Japanese deliveries in 2013. Although the migration of Japanese people from Kyushu area to the metropolitan areas has been supposed to contribute to a significant decrease of HTLV-1 carriers in Kyushu area, the estimated number of HTLV-1 carrier of pregnant women seemed to be increased in Kyushu area. In addition, the estimated rate of HTLV-1 carrier in pregnant women in Kyushu area was still significantly higher than that in the other areas (P < 0.01 by the Chi-square test). Therefore, there are still remaining problems concerning the locality for strategies for prevention of HTLV-1 vertical transmission in Japan.

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)0.015
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.197
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.180
GPT teacher head0.463
Teacher spread0.283 · 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.

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

Citations14
Published2015
Admission routes1
Has abstractyes

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