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A Meta-Analysis of Diagnostic Test Accuracy (DTA) of the Endoscopic Ultrasound (EUS) and Magnetic Resonance Cholangiopancreatography (MRCP) in Detecting Choledocholithiasis

2016· article· en· W2978374030 on OpenAlexaff
Yaser kh. Meeralam, Khalil Alshammarikhalil, Mohammad Yaghoobi

Bibliographic record

VenueThe American Journal of Gastroenterology · 2016
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineReceiver operating characteristicGold standard (test)Diagnostic odds ratioMeta-analysisEndoscopic ultrasoundMagnetic resonance cholangiopancreatographyRadiologyEndoscopic retrograde cholangiopancreatographyLikelihood ratios in diagnostic testingPublication biasNuclear medicineInternal medicinePancreatitis

Abstract

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Introduction: Published reports provide a wide range of sensitivity and specificity for EUS and MRCP in the diagnosis of the choledocholithiasis. However, a proper meta-analysis of DTA is lacking. Here, we aimed at determining and comparing the diagnostic accuracy of EUS and MRCP in detecting choledocholithiasis using appropriate methodology recommended by the Cochrane group. Methods: A comprehensive electronic literature search up to March 2016 was done by two reviewers for prospective cohort studies comparing EUS and MRCP to the gold standard in detecting choledocholithiasis. The gold standard was considered endoscopic retrograde cholangiopancreatography (ERCP), intra-operative cholangiography (IOC) or clinical follow up >3 months for negative cases. There was no restriction in terms of language, location or quality sof the studies. Abstracts, unpublished data, studies with insufficient data and pediatric studies were excluded. Quality of the included studies was measured using QUADAS-2 tool. Data analysis was done using DerSimonian Laird method (random effects model) on intention-to-treat data. Mantel-Haenszel or the DerSimonian Laird methods to estimate the overall diagnostic Odds Ratio (DOR) and hence to determine the best-fitting receiver operating characteristics (ROC) curve. Symmetrical ROC (sROC) was developed and area under curve (AUC) was calculated. Results: A total of five out of fourteen studies were included. Three studies were from Europe, one from Japan and one from the US. All studies had low risk of bias. The pooled sensitivity, specificity, DOR and AUC for EUS were 96.6(91.4-99.1)%, 89.7(83.3-94.3)%, 0.9771, 162.5(54.0-489.3), and 0.977 (SE:0.011,Q:0.932), respectively. The pooled sensitivity, specificity, DOR and AUC for MRCP were 87.1(79.6-92.6)%, 92.5(86.9-96.2)%, 79.0(23.8-262.2) and 0.95.2 (SE:0.027; Q:0.89), respectively. The difference between the AUC of diagnostic accuracy of EUS and MRCP was 0.0248 (SE:0.015) arithmetically in favor of EUS but it was not significantly different (p:0.10, two-tailed). Conclusion: Both EUS and MRCP provide very good diagnostic accuracy in detecting choledocholithiasis with slight arithmetical superiority with EUS. Individual participant data and meta-regression analyses may identify the characters that may benefit from one modality or the other.Figure 1Figure 2

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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