Evidence map›Paper›PMID 37158904›Full record

SynthesisVirology journal2023

Predictive values of procalcitonin for coinfections in patients with COVID-19: a systematic review and meta-analysis.

Shanchen Wei, Lina Wang, Lianjun Lin, Xinmin Liu

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Virology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.7field-weighted citation impact, top 9% 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

8 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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

4 authors at 1 institution in 1 country.

Shanchen WeiDepartment of Geriatrics, Peking University First Hospital, Xishiku Avenue No 8, Xicheng District, Beijing, 100034, China.
Lina WangDepartment of Geriatrics, Peking University First Hospital, Xishiku Avenue No 8, Xicheng District, Beijing, 100034, China.
Lianjun LinDepartment of Geriatrics, Peking University First Hospital, Xishiku Avenue No 8, Xicheng District, Beijing, 100034, China. 06474@pkufh.com.
Xinmin LiuDepartment of Geriatrics, Peking University First Hospital, Xishiku Avenue No 8, Xicheng District, Beijing, 100034, China. lxm2128@163.com.ORCID 0000-0002-3264-5255
Peking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo assess the ability of procalcitonin (PCT)-a promising marker for coinfections-to predict coinfections in patients with COVID-19.

methodsIn this systematic review and meta-analysis, PubMed, Embase, Web of Science, Cochrane, the China National Knowledge Infrastructure (CNKI), and Wanfang were searched to identify eligible studies (up to August 30, 2021). Articles that reported the predictive value of PCT for coinfections in patients with COVID-19 were included. Individual and pooled sensitivities and specificities were reported, and I

resultsFive studies involving a total of 2775 patients reported the predictive value of PCT for coinfections in patients with COVID-19. The sensitivity, specificity, and area under the curve of PCT in predicting coinfections in the pooled studies were 0.60 (95% CI 0.35-0.81, I

conclusionsAlthough PCT has limited predictive value for coinfections in patients with COVID-19, lower PCT levels seem to indicate a decreased probability of having a coinfection.

Indexed as

CoinfectionCOVID-19ChinaDatabases, FactualHumansProcalcitoninProcalcitoninCoinfectionsCOVID-19Meta-analysisPredictiveProcalcitonin

Identifiers

PMID37158904
PMCPMC10166029
OpenAlexW4375956012

What OpenQuestion 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 OpenQuestion graph.