Evidence map›Paper›PMID 37460869›Full record

ArticleJournal of clinical monitoring and computing2023

Surgical outcomes and patient-centred perioperative programs.

Elena Bignami, Serena Celoria, Valentina Bellini

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Journal of clinical monitoring and computing, 2023. 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
0.4field-weighted citation impact, top 34% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

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

3 authors at 1 institution in 1 country.

Elena BignamiAnesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy. elenagiovanna.bignami@unipr.it.ORCID 0000-0001-9985-4126
Serena CeloriaAnesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy.
Valentina BelliniAnesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy.
University of Parma · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Perioperative medicine is changing, and its goals are expanding. More and more attention is paid to the surgical experience and the patient's quality of life. Patient-reported data represent a useful tool in this context. Patient-reported outcomes measures (PROMs) and experience measures (PREMs) are among the most used categories. However, creating perioperative programs capable of integrating traditional perioperative data with these scales is not easy. New technologies, particularly artificial intelligence, thanks to their ability to recognise, interpret, process or simulate human feelings, emotions and moods, could provide the necessary tools to combine all perioperative aspects, placing the patients and their needs at the centre of the process.

Indexed as

Artificial IntelligenceQuality of LifeHumansPatient Reported Outcome MeasuresTreatment OutcomeArtificial intelligencePerioperative periodPerioperative programsPREMsPROMs

Identifiers

PMID37460869
OpenAlexW4384562075

What OpenQuestion holds

Textmetadata
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.