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Record W2075874074 · doi:10.1159/000074066

An Association of Maternal Age and Birth Weight with End-Stage Renal Disease in Saskatchewan. Sub-Analysis of Registered Indians and Those with Diabetes

2003· article· en· W2075874074 on OpenAlexaffabout
Roland Dyck, Helena Klomp, Leonard Tan, Mary Rose Stang

Bibliographic record

VenueAmerican Journal of Nephrology · 2003
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsSaskatchewan HealthUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineBirth weightEnd stage renal diseaseConfidence intervalPopulationGestational ageDiabetes mellitusLow birth weightOffspringPregnancyDiseaseObstetricsInternal medicinePediatricsEndocrinology

Abstract

fetched live from OpenAlex

AIMS: To determine links between birth related factors and end-stage renal disease (ESRD). METHODS: This 1:3 age, sex, and source population (registered Indians [SkRI] and other Saskatchewan people [OSkP]) matched case-control study, compared maternal age and parity, gestational age, low birth weight (LBW), and high birth weight (HBW), between subjects with and without ESRD. RESULTS: Of 1,162 subjects, 277 cases (48 SkRI and 229 OSkP) and 601 controls (112 SkRI and 489 OSkP) had birth weight information. A trend for increased LBW rates occurred among SkRI and OSkP cases compared to controls (10.4 vs. 5.3% and 6.6 vs. 4.3%), and was significant for OSkP female cases (OR 3.66; 95% confidence interval [CI] 1.05, 12.73). Higher HBW rates occurred in SkRI cases (14.6% compared to 11.6% controls; N/S), and 3/5 female SkRI diabetic ESRD (DESRD) cases were over 3,750 g compared to 1/14 controls (p < 0.05). Only maternal age >/=30 years was an independent predictor for ESRD, particularly for OSkP non-DESRD cases (OR 2.45; 95% CI 1.03, 5.8). Cases with older mothers had lower mean birth weights than controls (3,236 vs. 3,434 g; p = 0.005). CONCLUSIONS: Older maternal age may predispose offspring to ESRD through mechanisms that differ for DESRD versus non-DESRD, and that may relate to ethnicity.

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)0.000
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 score0.004
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.257
Teacher spread0.247 · 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

Citations25
Published2003
Admission routes2
Has abstractyes

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