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Record W2504057747 · doi:10.1111/1471-0528.14193

Use of gadolinium in placental <scp>MRI</scp> to improve diagnostic performance for abnormally invasive placentas in patients at risk

2016· letter· en· W2504057747 on OpenAlexaboutno aff

Bibliographic record

VenueBJOG An International Journal of Obstetrics & Gynaecology · 2016
Typeletter
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGadoliniumMagnetic resonance imagingOligohydramniosObstetricsPlacentaPregnancyUltrasoundRadiologyFetus

Abstract

fetched live from OpenAlex

Abnormally invasive placentation (AIP) is a rare but life-threatening condition in which placental tissue surpasses its normal location and abnormally invades the myometrium and neighbouring structures. It is an important cause of excessive postpartum haemorrhage. Its incidence is increasing owing to the growing number of caesarean sections being performed. The diagnosis is reached by the presence of placental ultrasound (US) or magnetic resonance imaging (MRI) signs suggestive of this condition. When the accuracy for the detection of AIP using US and MRI was compared, similar results were obtained. Nevertheless, the performance of both imaging techniques for the detection of AIP is suboptimal. False-negative cases of AIP may represent an increase in maternal morbimortality, as some crucial interventions that are indicated in suspected AIP may not be performed. The indication of a scheduled caesarean delivery in a tertiary referral centre with a multidisciplinary team significantly improves outcomes. False-positives for AIP represent unnecessary maternal–fetal morbidity. For this reason, there is an real need to improve the diagnostic imaging accuracy for AIP. In this study, the authors tested the hypothesis that gadolinium contrast could improve the diagnostic performance of MRI for AIP. One of the main strengths of the study is its novelty, as gadolinium is not frequently used during pregnancy owing to safety concerns. Uncertainty surrounds the risk of possible fetal effects because gadolinium contrast is a water-soluble agent and can cross the placenta into the fetal circulation and amniotic fluid. The main concern is that the duration of fetal exposure is unknown because it can be swallowed from amniotic fluid re-entering the fetal circulation, which could potentially increase the risk of fetal harm. Nevertheless, studies have shown how no adverse perinatal or neonatal outcomes were detected in cases that received this agent during the first trimester (De Santis et al. Acta Obstet Gynecol Scand 2007;86(1):99–101) and how no teratogenic effects were detected in animals exposed to it during pregnancy. Its use during pregnancy has recently been accepted by the European Society of Radiology, the American College of Obstetrics and Gynecology (Committee Opinion No 656; Obstet Gynecol 2016;127(2):e.75–80) and the Society of Obstetricians and Gynaecologists of Canada (Patenaude et al. J Obstet Gynaecol Can 2014;36(4):349–63) when benefits clearly outweigh the possible risks, as may be the case for AIP. The results provided in this study show how the use of gadolinium significantly increased the diagnostic sensitivity and specificity (+12.5% for both), suggesting that the use of this contrast agent could constitute a new reliable way of improving the detection rate of AIP in patients at risk. This should be considered a great achievement that may be helpful particularly in cases of inconclusive MRI scans and that may potentially reduce the morbimortality associated with AIP. Nevertheless, we should keep in mind that technical advances and upcoming studies may show how other MRI contrast agents that do not cross the placenta, or the use of additional MRI sequences, could also help to increase the accuracy of the detection of AIP (Siauve et al. Am J Obstet Gynecol 2015;213:S103) from an expectedly safer and non-invasive perspective. None declared. Completed disclosure of interests form available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0.002
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.280
Teacher spread0.258 · 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".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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