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Record W4311343408 · doi:10.3233/prm-210117

Preterm birth risk in women with skeletal dysplasias and short stature

2022· article· en· W4311343408 on OpenAlexaffabout
Deirdre O’Connor, Rebecca Menzies, Xingshan Cao, Anne Berndl

Bibliographic record

VenueJournal of Pediatric Rehabilitation Medicine · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsMedicineShort staturePremature birthPregnancyPopulationLive birthBirth rateGestational ageObstetricsPediatricsDemographyFertilityEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE: The primary objective was to estimate the risk of preterm delivery in a population of women with a skeletal dysplasia and short stature. The secondary outcome was to identify factors that increase the risk of preterm delivery. METHODS: A cross sectional survey was performed asking detailed pregnancy and reproductive health questions, aimed at a convenience sample of women who were little people, administered through Little People of America, Little People UK, Little People Canada, and the World Dwarf Games. Comparisons were made on gestational age at delivery between pregnancies with and without the outcomes. RESULTS: The survey had a response rate of 74% (117/158). There was a total of 55 eligible subjects who had 72 live births. Delivery prior to 37 weeks occurred in 19/72 live births, which equates to a preterm birth rate of 26.4%. Besides short stature, no single factor was identified that could solely explain the elevated preterm birth risk in the study population. CONCLUSION: The risk of preterm delivery in women with skeletal dysplasias and short stature is elevated compared to the general population. This information will assist healthcare providers in pregnancy management and counseling.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.005
GPT teacher head0.271
Teacher spread0.266 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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