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Preterm Premature Rupture of Membranes

2002· article· en· W4254347010 on OpenAlexaffabout
Sarah E. Ferguson, Graeme N. Smith, Mary Ellen Salenieks, Rory Windrim, Mark Walker

Bibliographic record

VenueObstetrics and Gynecology · 2002
Typearticle
Languageen
FieldMedicine
TopicPreterm Birth and Chorioamnionitis
Canadian institutionsToronto Public Health
Fundersnot available
KeywordsMedicinePromOdds ratioPremature rupture of membranesVitamin B12HomocysteineHemoglobinObstetricsConfidence intervalPregnancyGestational ageInternal medicinePhysiology

Abstract

fetched live from OpenAlex

In Brief OBJECTIVE To estimate if there were dietary or socioeconomic factors associated with preterm premature rupture of membranes (PROM). METHODS In this case-control study, women with preterm PROM (n = 46) were compared with healthy pregnant women matched for gestational age and vitamin supplementation. Measurements included fasting homocysteine, red blood cell folate, albumin, creatinine, and complete blood count. Dietary intake was determined by a food frequency questionnaire. Socioeconomic and demographic factors were recorded. RESULTS There were no differences between fasting homocysteine, red blood cell folate, and vitamin B12 levels anddietary intake between patients and controls. There was lower hemoglobin in women with preterm PROM compared with the controls (P < .001). There was a three-fold increased odds of having a total family income of less than $25,000 (Canadian) (odds ratio 3.1, 95% confidence interval 1.6, 6) in women with preterm PROM. CONCLUSION Preterm PROM is associated with low maternal hemoglobin and low socioeconomic status. There was no association with nutritional intake. The lower hemoglobin level may be a marker for subclinical infection. Preterm premature rupture of membranes is associated with indicators of lower socioeconomic status and low maternal hemoglobin levels and is not associated with dietary intake or serum levels of folate, homocysteine, or vitamin B12.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.228
Teacher spread0.213 · 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 designNot applicable
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

Citations31
Published2002
Admission routes2
Has abstractyes

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