Evidence map›Paper›PMID 42433856›Full record

ArticleAnnals of medicine and surgery (2012)2026

Effectiveness of a chronic care model-based transitional care program for patients with biliary drainage: a randomized controlled trial.

Ting Zhang, Long Zhang, Huiling Zhou, Haixia Chang

Abstract read
In one paragraph

Article in Annals of medicine and surgery (2012), 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

4 authors.

Ting ZhangDepartment of Hepatobiliary and Pancreatic Surgery, The Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Long ZhangDepartment of Occupational and Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China.
Huiling ZhouDepartment of Hepatobiliary and Pancreatic Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, China.
Haixia ChangDepartment of Hepatobiliary and Pancreatic Surgery, The Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.ORCID https://orcid.org/0009-0007-0941-4815

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Patients undergoing biliary drainage are at high risk of readmission. We evaluated a Chronic Care Model (CCM)-based transitional care program designed to improve post-discharge outcomes. Methods: This prospective, two-center randomized controlled trial assigned 300 patients requiring biliary drainage to an intervention (CCM-based transitional care) or control (usual care) group. Primary outcomes were 90-day hospital readmission and complication rates. Time-to-readmission was analyzed using the Kaplan-Meier method. Secondary outcomes included patient-reported readiness for hospital discharge, self-care behavior, and quality of life. Predictive models, including logistic regression and support vector machine (SVM), were developed to identify risk factors for readmission. Results: The intervention group had significantly lower 90-day readmission rates (16.3% vs. 37.1%; Conclusions: A structured, CCM-based transitional care program effectively reduces readmissions and complications while prolonging community-based stability for patients with biliary drainage. Furthermore, patient-reported metrics integrated into an SVM model accurately predict readmission risk, enabling targeted interventions.

Indexed as

cholelithiasischronic disease managementrandomized controlled trialssupport vector machinestransitional care

Identifiers

PMID42433856
PMCPMC13354461

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Registered trials

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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.