Evidence map›Paper›PMID 35744050›Full record

ReviewMedicina (Kaunas, Lithuania)2022

Predicting Severity of Acute Pancreatitis.

Dong Wook Lee, Chang Min Cho

Open access · goldAbstract readReview
In one paragraph

Review in Medicina (Kaunas, Lithuania), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
16.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

44 citing papers in PubMed, 1 synthesis or guideline pooled it, 85 citations in OpenAlex.

  1. Pooled it
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  19. Admission hematocrit and fluctuating blood urea nitrogen levels predict the efficacy of blood purification treatment in severe acute pancreatitis patients.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Dong Wook LeeDepartment of Internal Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Korea.
Chang Min ChoDepartment of Internal Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Korea.
Kyungpook National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute pancreatitis has a diverse etiology and natural history, and some patients have severe complications with a high risk of mortality. The prediction of the severity of acute pancreatitis should be achieved by a careful ongoing clinical assessment coupled with the use of a multiple-factor scoring system and imaging studies. Over the past 40 years, various scoring systems have been suggested to predict the severity of acute pancreatitis. However, there is no definite and ideal scoring system with a high sensitivity and specificity. The interest in new biological markers and predictive models for identifying severe acute pancreatitis testifies to the continued clinical importance of early severity prediction. Although contrast-enhanced computed tomography (CT) is considered the gold standard for diagnosing pancreatic necrosis, early scanning for the prediction of severity is limited because the full extent of pancreatic necrosis may not develop within the first 48 h of presentation. This article provides an overview of the available scoring systems and biochemical markers for predicting severe acute pancreatitis, with a focus on their characteristics and limitations.

Indexed as

Pancreatitis, Acute NecrotizingAcute DiseaseBiomarkersHumansPredictive Value of TestsPrognosisRetrospective StudiesSeverity of Illness IndexBiomarkersacute pancreatitispredicting factorsseverity

Identifiers

PMID35744050
PMCPMC9227091
OpenAlexW4282553315

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.