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Record W7162029558 · doi:10.82308/20512

Predictors of failed medical management in women with early pregnancy loss

2015· dissertation· en· W7162029558 on OpenAlexaboutno aff
Melissa Lavecchia

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsnot available
Fundersnot available
KeywordsPregnancyMisoprostolCurettageGestational ageLogistic regressionEarly Pregnancy LossRetrospective cohort studyPopulationCohort study

Abstract

fetched live from OpenAlex

Objectives: While medical management has been used to treat early pregnancy loss for over two decades, there remains a paucity of reliable predictors of treatment efficacy. The principal goal of this thesis is to identify new factors influencing medical management failure in women with first trimester pregnancy loss. Two objectives will be discussed. The first aim is to assess the effect of menstrual gestational age on the efficacy of misoprostol, in a population of women with a low sonographic gestational age. The second objective is to assess the value of uterine content ultrasound (US) measurements in predicting medical management failure in women with early pregnancy loss.Research design and methods: Two retrospective observational studies were conducted, each corresponding to a thesis objective. Both studies are based on a cohort of women with early pregnancy loss having visited the Emergency Department (ED) of the Jewish General Hospital, Montreal, Canada, between 2011 and 2013. Only women having been discharged with an outpatient prescription for misoprostol were included in out studies. With respect to the first objective, women had to have an US gestational age below 8 weeks. Exposure of interest consisted of menstrual age. In regards to the second objective, all women with US imaging available for review were included. Exposure of interest was uterine content measurements including: content anteroposterior distance (CAPD), content longitudinal distance, content transverse distance and uterine content volume. For both studies, logistic regression was used to estimate the effect of exposure on failed medical management defined as need for dilatation and curettage (D&C) or unplanned return to the ED (URED). Results: Increasing menstrual age was associated with an increased risk of D&C and URED. Specifically, risk of D&C and URED was 13.64% at ≤ 8 weeks and 26.32% at > 8 weeks, p < 0.05. Among uterine content measurements evaluated, CAPD was found to be independently associated with D&C and URED. When using an APD cutoff of 15mm, women were more likely to require D&C, 2.65 (1.31 - 5.36), p < 0.01 and to have an URED, 2.59 (1.41 - 4.79), p < 0.01. Conclusion: Both clinical and sonographic factors play a role in predicting successful medical management of early pregnancy loss. Increasing menstrual gestational age is associated with an increased risk of failed medical management regardless of US estimated gestational age. Furthermore, women with a uterine CAPD below 15mm on US should be considered good candidates for successful medical management. Although there is a need for further validation, combining current predictors of misoprostol efficacy with menstrual gestational age and ultrasonographic measurements of uterine CAPD may be helpful in selecting patients with first trimester pregnancy failure that can safely be managed medically.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.298
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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