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Record W4385564354 · doi:10.1159/000531791

The Management of Acardiac Twinning: Twin Reverse Arterial Perfusion Sequence – An International Survey

2023· article· en· W4385564354 on OpenAlexaff
Saulo Molina‐Giraldo, Natalia A. Torres-Valencia, Anthony Johnson, Liesbeth Lewi, Greg Ryan, Waldo Sepúlveda

Bibliographic record

VenueFetal Diagnosis and Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicinePerfusionRadiofrequency ablationUmbilical arteryGestational ageInternal medicineCardiologyFetusAblationSurgeryPregnancy

Abstract

fetched live from OpenAlex

INTRODUCTION: The optimal approach and therapy method for the acardiac twin with a reverse arterial perfusion sequence has not yet been established. The aim of this study was to determine the clinical practice patterns among international fetal therapy units in their management of these cases. METHODS: A survey was sent to fetal centers across the world via email between December 2020 and December 2021. RESULTS: Responses were obtained from 77% contacted centers. The most frequent ultrasound variables used in the evaluation of twin reverse arterial perfusion sequence include echocardiographic assessment of the pump twin and umbilical artery Doppler waveforms in the acardiac and pump twins, in 90% and 80% of the centers, respectively. Most centers in Europe and Latin America propose an in utero intervention in all cases. Most centers in Europe and Latin America prefer interstitial laser ablation, whereas radiofrequency ablation (RFA) is preferred in North America. The earliest gestational age for an intervention is on mean 13 weeks in Europe, which is earlier than the other geographic areas (p = 0.001). CONCLUSIONS: Most centers agreed that antenatal evaluation should include echocardiography along with the UA Doppler waveform measurements, and the most frequently used interventions were interstitial laser ablation or RFA at a median between 14 and 26 weeks.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.065
GPT teacher head0.338
Teacher spread0.273 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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