Evidence map›Paper›PMID 42371473›Full record

ArticleJournal of multidisciplinary healthcare2026

Development and Expert Consensus of an Evidence-Based Strategy to Prevent Postoperative Delirium in Malnourished Cancer Patients.

Yi-Fang Ma, Qi Wu

Abstract read
In one paragraph

Article in Journal of multidisciplinary healthcare, 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
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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.

Yi-Fang MaDepartment of Nursing, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China.ORCID 0000-0002-1752-421X
Qi WuDepartment of Nursing, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China.ORCID 0000-0003-0499-0577

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cancer patients with malnutrition are at high risk for postoperative delirium. To develop a preliminary prevention strategy for postoperative delirium in malnourished cancer patients based on literature search and Delphi expert consultations. Methods: Between August 2023 and January 2024, we followed the PIPOST (Population, Intervention, Professional, Outcome, Setting, and Type of evidence) framework to formulate clinical questions and retrieved relevant literatures using the 6S model (Systems, Summaries, Synopses of Syntheses, Syntheses, Synopses of Single Studies, and Single Studies). Based on the evidence, a preliminary strategy for preventing postoperative delirium in malnourished cancer patients was developed. Following two rounds of Delphi expert consultations, the final version of the strategy was created to develop and refine the proposed prevention strategy for postoperative delirium. Results: The initial literature search yielded 6218 articles, from which 20 core studies were ultimately included. The response rates for the two rounds of Delphi consultations were 96.0% and 100%, with authority coefficient 0.912 and expert concordance coefficient (Kendall's W) increased from 0.398 to 0.465 (p < 0.01). The finalized prevention strategy comprised four categories, 10 sections, and 27 items. Conclusion: An evidence-based strategy for preventing postoperative delirium has been developed preliminarily. Further studies are warranted to evaluate its clinical feasibility and efficacy to reduce the incidence of postoperative delirium in malnourished cancer patients.

Indexed as

cancerDelphi consultationevidence-based approachmalnutritionpostoperative deliriumprevention strategy

Identifiers

PMID42371473
PMCPMC13310503

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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.