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Record W4388562621 · doi:10.1097/md.0000000000035743

Risk factors for rehospitalization within 90 days in patients with total joint replacement: A meta-analysis

2023· review· en· W4388562621 on OpenAlexaboutno aff
Liping Zeng, Hua Cai, Aling Qiu, Dongfu Zhang, Lingying Lin, Xirong Lian, Manli Chen

Bibliographic record

VenueMedicine · 2023
Typereview
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineTotal joint replacementDeep veinSurgeryThrombosis

Abstract

fetched live from OpenAlex

BACKGROUND: The risk factors influencing the readmission within 90 days following total joint replacement (TJR) are complex and heterogeneous, and few systematic reviews to date have focused on this issue. METHODS: Web of Science, Embase, PubMed, and Chinese National Knowledge Infrastructure databases were searched from the inception dates to December 2022. Relevant, published studies were identified using the following keywords: risk factors, rehospitalization, total hip replacement, total knee replacement, total shoulder replacement, and total joint replacement. All relevant data were collected from the studies that meet the inclusion criteria. The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: Of 68,336 patients who underwent TJR, 1,269,415 (5.4%) were readmitted within 90 days. High American Society of Anesthesiologists (ASA) class (OR, 1.502; 95%CI:1.405-1.605; P < .001), heart failure (OR,1.494; 95%CI: 1.235-1.754; P < .001), diabetes (OR, 1.246; 95%CI:1.128-1.377; P < .001), liver disease (OR, 1.339; 95%CI:1.237-1.450; P < .001), drinking (OR, 1.114; 95%CI:1.041-1.192; P = .002), depression (OR, 1.294; 95%CI:1.223-1.396; P < .001), urinary tract infection (OR, 5.879; 95%CI: 5.119-6.753; P < .001), and deep vein thrombosis (OR, 10.007; 95%CI: 8.787-11.396; P < .001) showed statistically positive correlation with increased 90-day readmissions after TJR, but high blood pressure, smoking, and pneumonia had no significant association with readmission risk. CONCLUSION: The findings of this review and meta-analysis will aid clinicians as they seek to understand the risk factors for 90-day readmission following TJR. Clinicians should consider the identified key risk factors associated with unplanned readmissions and develop strategies to risk-stratify patients and provide dedicated interventions to reduce the rates of readmission and enhance the recovery process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.021
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0150.065
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.096
GPT teacher head0.338
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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