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Record W2769181141 · doi:10.2147/ijwh.s115515

Adverse maternal outcomes and birth weight discordance in twin gestation: British Columbia, Canadian data

2017· article· en· W2769181141 on OpenAlexaffabout
Shayesteh Jahanfar, Kenneth Lim

Bibliographic record

VenueInternational Journal of Women s Health · 2017
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsB.C. Women's Hospital & Health Centre
Fundersnot available
KeywordsMedicineObstetricsPregnancyPreeclampsiaOddsOdds ratioTwin PregnancyBirth weightGestationFetal growthPopulationCohortRetrospective cohort studyCohort studyLogistic regressionInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to determine whether twin pregnancies with birth weight discordance were associated with higher rates of maternal morbidities. STUDY DESIGN: A large retrospective population-based cohort study of twins born in British Columbia, Canada, from 2000 to 2010 was performed. Maternal morbidities and growth discordant were evaluated. RESULTS: There were 6,328 twin deliveries during the study period. Pregnancies carrying growth-discordant twins had higher frequencies of hypertension disorders, preterm labor, and cesarean delivery compared with growth-concordant twins. They also stayed longer than 3 days in hospital. Multivariate generalized estimating equation modeling found higher odds of preeclampsia, pregnancy-induced hypertension, preterm delivery, and cesarean delivery in mothers carrying growth-discordant twins compared with those carrying growth-concordant category. The modeling also resulted in higher odds in the length of stay longer than 3 days in mothers carrying growth-discordant twins compared with those carrying growth-concordant twins after adjustment for chorionicity. CONCLUSION: Maternal complications are associated with growth discordance. Screening for birth weight discordance during pregnancy may alert clinicians to predict subclinical maternal conditions.

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 score0.264
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.325
Teacher spread0.303 · 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

Citations17
Published2017
Admission routes2
Has abstractyes

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