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Record W2979602962 · doi:10.1111/apa.15053

Later puberty onset among chronically undernourished adolescents living in a Karachi slum, Pakistan

2019· article· en· W2979602962 on OpenAlexaff
Susan C. Campisi, Khadija Nuzhat Humayun, Arjumand Rizvi, Wendy Lou, Olle Söder, Zulfiqar A Bhutta

Bibliographic record

VenueActa Paediatrica · 2019
Typearticle
Languageen
FieldMedicine
TopicHypothalamic control of reproductive hormones
Canadian institutionsSickKids FoundationCentre for Global Health ResearchHospital for Sick ChildrenPublic Health OntarioUniversity of Toronto
FundersSixth Framework Programme
KeywordsMedicinePediatricsDemographyYoung adultDelayed pubertyBreast feedingInternal medicine

Abstract

fetched live from OpenAlex

AIM: To assess factors associated with the timing of puberty onset (Tanner Stage Breast 2/Genital 2) among adolescents living in an urban slum in Karachi, Pakistan. METHODS: Girls enrolled at 8-10 years (n = 1009) and boys 9-11 years (n = 863) were followed every 6 months from 2006 to 2010. Parametric survival analysis for interval-censored data was used to estimate median age at puberty onset and assess the association between exposures and timing of puberty onset. RESULTS: The overall median age at Tanner Stage Breast 2 (AAB2) was 10.1 years (95% CI: 10.1; 10.5), and the median age at Tanner Stage Genital 2 (AAG2) was 10.1 years (95% CI: 10.1; 10.6). Stunting delayed AAB2 1 year and AAG2 about 6 months when compared to non-stunted peers. In the multivariable model, after adjusting for age at enrolment, stunting, thinness and vitamin A deficiency (VAD) were significantly associated with delayed AAB2, while stunting and anaemia were significantly associated with delayed AAG2. CONCLUSION: Among adolescents living in the Karachi slum, stunting and highly prevalent anaemia delayed AAG2, while stunting, thinness and VAD delayed AAG2. Parental and household factors were not significantly associated with the timing of puberty onset.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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