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Record W2462097014 · doi:10.14740/jmc.v7i7.2484

A Case of Bilateral Ectopic Pregnancy Following Intra-Uterine Insemination

2016· article· en· W2462097014 on OpenAlexvenueno aff
Wen Shan Sim, Elis Yuexian Lee, Pamela Yu Fen Tan

Bibliographic record

VenueJournal of Medical Cases · 2016
Typearticle
Languageen
FieldMedicine
TopicEctopic Pregnancy Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEctopic pregnancySalpingostomyObstetricsMethotrexateHeterotopic pregnancyGynecologyAdnexal massPregnancySurgerySalpingectomy

Abstract

fetched live from OpenAlex

We describe a case of a 31-year-old lady with primary subfertility who conceived only after her third intra-uterine insemination. She was diagnosed to have a right ectopic pregnancy which failed methotrexate treatment. Diagnostic laparoscopy confirmed bilateral ectopic pregnancies. A review was conducted for any consensus reached on diagnosis and optimal management of bilateral ectopic pregnancies after artificial reproductive techniques (ARTs). A search on Pubmed was carried out for publications in peer-reviewed journals with the following keywords: bilateral tubal ectopic pregnancy, heterotopic. Ten suitable publications were retrieved. Bilateral tubal ectopic pregnancy is rare, often missed on radiology and an unexpected finding at surgery. There should be a high index of suspicion for bilateral ectopic pregnancies after ART. Poor correlation with B-hCG levels further confuses diagnostic accuracy. It is prudent to ensure accurate diagnosis of bilateral ectopic pregnancies and appropriate intervention to reduce morbidity and mortality. Salpingostomy should be considered in patients with subfertility to preserve the possibility of future pregnancies. J Med Cases. 2016;7(7):286-290 doi: http://dx.doi.org/10.14740/jmc2484w

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)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.030
GPT teacher head0.334
Teacher spread0.304 · 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 designCase report
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

Citations0
Published2016
Admission routes1
Has abstractyes

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