Is endoscopic retrograde cholangiopancreatography safe during pregnancy?
Bibliographic record
Abstract
Sir, We read with great interest the recent systematic review of the safety of endoscopic retrograde cholangiopancreatography (ERCP) in pregnancy published by Azab et al.[1] that compared the outcomes of radiation and nonradiation ERCP. Dr Azab and colleagues reported a well-conducted research. The topic is important for clinicians and policymakers as the safety of ERCP in pregnancy remains controversial. However, we have several concerns about this study. First, systematic reviews are significant for health care practitioners who need to make informed decisions. Improper design of clinical research may result in multiple biases.[2] In a systematic review, the purpose of bias assessment is to determine the strengths and limitations of qualified research and to grade the strength of evidence for a given problem.[2] In general, the risk of bias should be assessed by using the Newcastle–Ottawa Scale (NOC)[3] for observational studies and the Cochrane Risk of Bias Tool[2] for randomized controlled trials. Nevertheless, it is inappropriate for authors to use the NOS only to assess the risk of bias in the included study. Second, fixed-effect model and random-effect model are two kinds of statistical models of meta-analysis, and reliable statistical data can be obtained by choosing the appropriate model. The I2 test is applied to measure the statistical heterogeneity across relative studies. I2 statistic with an I2>50% indicates significant heterogeneity.[2] For another, in ordinary case, if significant heterogeneity is observed, the random-effect model is utilized, otherwise, statistical analysis is performed using a fixed-effect model.[24] However, the random-effect model can be used entirely in some cases. For example, in conditions where protocol or participants of the included studies are heterogeneous. However, this article does not specify which statistical model is used. In addition, we suggest that the reasons for choosing statistical models should be explained in this article. Third, in the results of this meta-analysis, the combined incidence of overall adverse events in pregnant women was 15.9% (95% CI = 0.132–0.19).[1] The results were statistically significant (P < 0.01). Overall adverse events were more common in the ERCP group (as shown in the original manuscript). Conversely though, the conclusion of the paper is that ERCP is a safe procedure during pregnancy. In conclusion, the results and findings of this research should be interpreted with caution. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".