Evidence map›Paper›PMID 42523715›Full record

ArticleIndian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine2026

From Intensive Care Unit Prediction to Intensive Care Unit Accountability: A Global Readiness Passport for Machine-learning Prognostication.

Mulavagili Vijayasimha, Mulavagili Srikanth, Ravinder Kaur, Bhawana Thukral, Kanakala P Malini, Simranpreet Kaur

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine, 2026. 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

6 authors.

Mulavagili VijayasimhaDepartment of Biochemistry, University Institute of Allied Health Sciences, Chandigarh University, Mohali, Punjab, India.ORCID https://orcid.org/0000-0003-2038-7006
Mulavagili SrikanthDepartment of Microbiology, Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences, Rohtak, Haryana, India.ORCID https://orcid.org/0009-0005-8488-6041
Ravinder KaurUniversity Institute of Allied Health Sciences, Chandigarh University, Mohali, Punjab, India.ORCID https://orcid.org/0000-0002-1697-9562
Bhawana ThukralDepartment of Nutrition and Dietetics, Chandigarh University, Mohali, Punjab, India.ORCID https://orcid.org/0009-0007-4145-2766
Kanakala P MaliniDepartment of Radiology, School of Allied and Healthcare Sciences, Centurion University of Technology and Management, Vizianagaram, Andhra Pradesh, India.ORCID https://orcid.org/0009-0005-4085-0055
Simranpreet KaurDepartment of Medical Laboratory Technology, Department of Biochemistry, University Institute of Allied Health Sciences, Chandigarh University, Mohali, Punjab, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Artificial intelligenceIntensive care unitMachine learning

Identifiers

PMID42523715
PMCPMC13407470

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.