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[Epidemiologic characteristics and risk factors for congenital hypothyroidism from 1989 to 2014 in Beijing].

2016· article· en· W2528812267 on OpenAlexaff
Yang Hh, Qiu L, Zhao Jq, Ningxi Yang, Gong Lf, Kong Yy

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsNewborn Screening Ontario
Fundersnot available
KeywordsMedicineIncidence (geometry)Congenital hypothyroidismBeijingPediatricsEpidemiologyChristian ministryThyroidInternal medicineChina

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the epidemiologic characteristics and risk factors for congenital hypothyroidism (CH) in Beijing between the years 1989 and 2014. METHODS: Information on neonatal screening, and diagnoses and treatment of CH from 1989 to 2014 were obtained from the database of the Beijing Neonatal Screening Center. The screening parameter was thyroid-stimulating hormone (thyrotropin; TSH), which was measured by radioimmunoassay (RIA) from 1989 to 1995, enzyme-linked immunosorbent assay (ELISA) from 1996 to 2003, and time-resolved fluorescence immunoassay (DELFIA(®)) from 2004 to 2014. The cutoff value of each screening method was set as the international standard for the corresponding years (20 mIU/L from 1989 to 1995 and 10 mIU/L from 1996 to 2014). CH was diagnosed using "The Technical Specification of Diagnosis and Treatment of Phenylketonuria and Congenital Hypothyroidism" , published in 2010 by the Ministry of Health of the People's Republic of China. Data on live birth infants were obtained from Beijing obstetric quality reports. The incidence of CH using different screening methods was compared, and trends in annual incidence were analyzed. To exclude the influence of different screening methods, data from the years 2004 to 2014 were used to identify the risk factors for CH. RESULTS: Between 1989 and 2014, the incidence of CH in Beijing was 36.7 per 100 000 individuals, with permanent CH (PCH) and transient CH (TCH) having incidences of 16.4 per 100 000 and 15.9 per 100 000, respectively. The annual incidence of CH increased from 11.2 per 100 000 in 1989 to 51.0 per 100 000 in 2014 (χ(2)=119.02, P<0.001), with PCH increasing from 5.6 to 16.0 per 100 000 (χ(2)=34.38, P<0.001) and TCH increasing from 5.6 to 13.0 per 100 000 (χ(2)=26.93, P<0.001). Among the PCH cases, 70.44% (255/362) were thyroid dysgenesis or ectopic glands, while the other 29.56% (107/362) were dyshormonogenesis. Between 2004 and 2014, the incidence of CH in females (51.7/100 000) was higher than in males (37.0/100 000), and it was higher in post-term (334.5/100 000) and preterm births (77.8/100 000) than that in term births (41.4/100 000). It was also higher in the low birth weight (87.7/100 000) than the normal (42.4/100 000) and high birth weight (42.6/100 000) populations. CONCLUSIONS: Between 1989 and 2014, there was a tendency towards an increase in the overall incidence of CH, and the incidence of both PCH and TCH in Beijing. Female sex, preterm birth, older gestational age, low birth weight, and preterm birth were risk factors affecting the incidence of CH in Beijing.

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)0.000
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 score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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".

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Citations6
Published2016
Admission routes1
Has abstractyes

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