Evidence map›Paper›PMID 41988470›Full record

ArticleFrontiers in nutrition2026

Application of an ACE star model-based evidence-based enteral nutrition management protocol in neurocritical care patients.

Rong Yuan, Lei Liu, Jiao Mi, Xue Li, Shifang Mao

Abstract read
In one paragraph

Article in Frontiers in nutrition, 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

5 authors.

Rong YuanNeurological Intensive Care Unit, Deyang People's Hospital, Deyang, China.
Lei LiuNeurological Intensive Care Unit, Deyang People's Hospital, Deyang, China.
Jiao MiNeurological Intensive Care Unit, Deyang People's Hospital, Deyang, China.
Xue LiNeurological Intensive Care Unit, Deyang People's Hospital, Deyang, China.
Shifang MaoDepartment of Nursing, Affiliated Hospital of Southwest Medical University, Luzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the efficacy of an evidence-based enteral nutrition management protocol guided by the ACE Star model in neurocritical care patients. Methods: We collected clinical data from 82 patients admitted to the neurocritical care unit of a tertiary hospital in Sichuan Province. The baseline group comprised 40 patients who met the inclusion and exclusion criteria and were admitted between June and September 2024. The evidence-based practice group included 42 patients meeting the same criteria and admitted between January and April 2025. We compared the two groups with respect to the incidence of feeding intolerance, rates of achieving target feeding goals, changes in serum albumin and prealbumin levels, and healthcare providers' adherence to standardized enteral nutrition management protocols. Results: Following implementation of the evidence-based protocol, the incidence of feeding intolerance decreased significantly compared with the baseline period. Improvements were also observed in serum prealbumin and albumin levels and in the proportion of patients achieving target feeding goals. Furthermore, adherence to standardized enteral nutrition practices among healthcare personnel increased significantly after implementation ( Conclusion: A standardized enteral nutrition management protocol based on the ACE Star evidence-based model reduces feeding intolerance, enhances nutritional status, and supports recovery in neurocritical care patients.

Indexed as

ACE star modelenteral nutritionevidence-based practicefeeding intoleranceneurocritical care

Identifiers

PMID41988470
PMCPMC13076138

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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.