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Record W4409168573 · doi:10.1177/20543581251324587

Ambulance Service Utilization by Kidney Transplant Recipients

2025· article· en· W4409168573 on OpenAlexaffabout
Kaveh Masoumi-Ravandi, Amanda J. Vinson, Aran Thanamayooran, Judah Goldstein, Thomas A. A. Skinner, Karthik Tennankore

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineEmergency departmentEmergency medicineHazard ratioRetrospective cohort studyProportional hazards modelPopulationConfidence intervalKidney transplantationCohortTransplantationInternal medicine

Abstract

fetched live from OpenAlex

Background: Compared with the general population, kidney transplant recipients (KTRs) frequently visit the emergency department (ED), but much less is known about the characteristics of ED presentations requiring ambulance transport and the impact on subsequent outcomes for KTRs. Objectives: To identify predictors of ambulance transport to the ED (ambulance-ED) and outcomes (graft failure and mortality) for those who experienced an ambulance-ED event in a cohort of KTRs. Design: Retrospective cohort study of incident, adult KTRs receiving a transplant from 2008 to 2020. Setting: Nova Scotia, Canada. Patients: Adult (≥18 years), Nova Scotian KTRs affiliated with the Atlantic Canada Multi-Organ Transplant Program. Measurements: Ambulance-ED events were captured for all transplant recipients (following the day of discharge from their initial transplant admission) using electronic records (provided by Emergency Health Services, the sole provider of emergency medical services for Nova Scotia). Ambulance-ED was defined as ambulance transport to the ED following a 911 call; interfacility transfers were excluded. Predictors of ambulance-ED included recipient, donor, immunological, and perioperative characteristics (pertaining to the initial admission for kidney transplantation). Outcomes included graft failure and mortality. Methods: Predictors of ambulance-ED were analyzed using a multivariable negative binomial regression model and reported using incidence rate ratios (IRRs) and 95% confidence intervals (CIs). The risk of death/graft failure for those with an ambulance-ED within 30 days of hospital discharge following transplantation was analyzed using an adjusted Cox survival analysis and reported using hazard ratios (HRs) and 95% CIs. Results: A total of 418 patients received a transplant during the study period. A total of 179 (42.8%) experienced one or more ambulance-ED events. Female sex (IRR = 1.60; 95% CI = 1.12-2.29), kidney failure secondary to diabetes (IRR = 2.52; 95% CI = 1.19-5.31), and donor age ≥45 (IRR = 1.50; 95% CI = 1.04-2.15) were all associated with ambulance-ED. There was no significant increase in the risk of death/graft failure for those that experienced ambulance-ED within 30 days of hospital discharge following transplantation (HR = 1.31; 95% CI = 0.44-3.94). Limitations: A limitation of this study was that ambulance-ED is not a perfect surrogate marker of acute care needs in a population. Important determinants of health such as living situation and socioeconomic status were not available in this data set. Conclusions: This study highlights the burden of ambulance use for KTRs and provides insight into the need for more optimal follow-up in certain patient subgroups who are at particularly high risk.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.195
Threshold uncertainty score0.387

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.310
Teacher spread0.287 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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