Evidence map›Paper›PMID 42046826›Full record

ArticleSage open pediatrics

Postoperative Pain in Children: A Real Emergency.

Alessandro Vittori, Marco Cascella

Abstract read
In one paragraph

Article in Sage open pediatrics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Alessandro VittoriDepartment of Anesthesia, Critical Care and Pain Medicine, Ospedale Pediatrico Bambino Gesù IRCCS, Rome, Italy.ORCID https://orcid.org/0000-0002-2377-3765
Marco CascellaUniversity of Salerno, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The topic of acute pain, especially post-operative pain, risks being unfairly relegated to the background. Although significant progress has been made in pediatric anesthesia, with a drastic reduction in perioperative mortality and morbidity, much remains to be done in terms of organization and training. Artificial intelligence can potentially help here. Combining data from large, well-structured datasets can provide predictive models that guide patients at higher risk of developing pain. Having reliable alerts would allow resources to be used not only more effectively, but also more rationally. Furthermore, artificial intelligence could help us diagnose and measure the pain of all those patients with cognitive barriers, exploiting nonverbal communication.

Indexed as

anesthesiachildrenpainpediatric anesthesiapediatric pain

Identifiers

PMID42046826
PMCPMC13110336

What OpenQuestion holds

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LicenceCC BY-NC
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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.