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Record W2794002476 · doi:10.1177/1062860618754702

The Impact of a Transition of Care Program on Acute Myocardial Infarction Readmission Rates

2018· article· en· W2794002476 on OpenAlexaff
Jeffrey A. Marbach, Drew Johnson, Juergen Kloo, Amit Vira, Scott W. Keith, Walter K. Kraft, Natalie Margules, David J. Whellan

Bibliographic record

VenueAmerican Journal of Medical Quality · 2018
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineMyocardial infarctionLogistic regressionOdds ratioOddsEmergency medicineAcute carePropensity score matchingHospital readmissionConditional logistic regressionTransitional careHospital dischargeInternal medicineIntensive care medicineHealth care

Abstract

fetched live from OpenAlex

Hospital discharge is a high-risk time period, and acute myocardial infarction (AMI) patients often have early readmissions. The authors hypothesized that a multifaceted AMI care coordination program would reduce early hospital readmission rates. The outcomes of patients receiving care coordination (n = 304) were compared to patients receiving standard care (n = 192). Multivariable analyses of the outcomes were conducted by conditional logistic regression of propensity score matched sets. The primary outcome-hospital readmission within 30 days of discharge-occurred in 18% of standard care patients and 11.8% of care coordination patients. Patients receiving care coordination demonstrated a 48% reduction in odds of readmission within 30 days (odds ratio = 0.52; P = .04; 95% CI = 0.28-0.97). These results are the first to demonstrate that inclusion in an AMI-specific care coordination program is associated with a significantly lower risk of 30-day hospital readmission.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.434
Teacher spread0.412 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2018
Admission routes1
Has abstractyes

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