Evidence map›Paper›PMID 40821736›Full record

ArticleJournal of orthopaedics2025

Prediction of non-routine discharge following single-level cervical disc arthroplasty using machine learning.

Paul G Mastrokostas, Leonidas E Mastrokostas, Jason M Dayan, Aaron B Lavi, Eli Berglas, Ahmed K Emara, Jad Bou Monsef, Afshin E Razi, Mitchell K Ng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of orthopaedics, 2025. 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

9 authors.

Paul G MastrokostasDepartment of Orthopaedic Surgery, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.
Leonidas E MastrokostasDepartment of Orthopaedic Surgery, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.
Jason M DayanDepartment of Orthopaedic Surgery, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.
Aaron B LaviDepartment of Orthopaedic Surgery, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.
Eli BerglasDepartment of Orthopaedic Surgery, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.
Ahmed K EmaraCleveland Clinic, Department of Orthopaedic Surgery, Cleveland, OH, USA.
Jad Bou MonsefDepartment of Orthopaedic Surgery, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.
Afshin E RaziMaimonides Medical Center, Department of Orthopaedic Surgery, Brooklyn, NY, USA.
Mitchell K NgMaimonides Medical Center, Department of Orthopaedic Surgery, Brooklyn, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Non-routine discharge following single-level cervical disc arthroplasty (CDA) is associated with increased morbidity and healthcare burden. Identifying key predictors can improve perioperative planning and patient outcomes. The aim of this study is to predict non-routine discharge following single-level CDA and identify key discharge predictors. Methods: A cohort of 14,315 single-level CDA patients was identified from the National Inpatient Sample (NIS) database from 2016 to 2020. Eight machine learning models predicted non-routine discharge based on characteristics such as age, length of stay (LOS), comorbidities, and surgical factors. Chi-square and t-tests compared categorical and continuous outcomes, with significance set at the 0.05 level. Results: Significant predictors of non-routine discharge included psychoses (1.5 % vs. 0.4 %, Conclusion: Machine learning models, especially AdaBoost and Gradient Boosting, effectively predicted non-routine discharge after single-level CDA. Key predictors included psychoses, extended hospital stays, and older age. Recognizing these factors may support targeted interventions to enhance patient outcomes.

Indexed as

Cervical disc arthroplastyComorbiditiesDischarge dispositionMachine learningNational inpatient sample

Identifiers

PMID40821736
PMCPMC12354794

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.