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Record W4402882772 · doi:10.1101/2024.09.25.24314385

Emergency department revisits at thirty days are modestly explained by caregiver burden: a prospective cohort study

2024· preprint· en· W4402882772 on OpenAlexafffundabout
Nathalie Germain, Annie Toulouse-Fournier, Rawane Samb, Émilie Côté, Vanessa Couture, Stéphane Turcotte, Michèle Morin, Yves Couturier, Lucas B. Chartier, Nadia Sourial, Samir K. Sinha, Don Melady, M. Hardy, Richard Fleet, France Légaré, Denis Roy, Holly O. Witteman, Éric Mercier, Josée Rivard, Marie‐Josée Sirois, Joanie Robitaille, Patrick Archambault

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsCentre Hospitalier de l’Université de MontréalMount Sinai HospitalSinai Health SystemUniversity of TorontoUniversity Health NetworkUniversité LavalUniversité de MontréalSchwartz/Reisman Emergency Medicine InstituteUniversité de Sherbrooke
FundersUniversité Laval
KeywordsEmergency departmentProspective cohort studyCohortMedicineGerontologyEmergency medicinePsychologyPsychiatrySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Structured Abstract Importance Caregivers play a protective role in emergency department (ED) care transitions. When the demands of caregiving result in caregiver burden, ED returns can ensue. Objective We developed models describing how caregiver burden may predict ED revisits and admissions up to thirty days after discharge. Design This prospective cohort study nested within the LEARNING WISDOM clinical trial included older adults and their caregivers who underwent a transition of care from one of four EDs in Québec, Canada between January 1st, 2019, and December 21st, 2021. Setting This study occurred within an integrated health multi-site organization consisting of four acute care hospitals. Participants Patients aged 65 years or older who were discharged back to the community from the ED observation unit after being triaged to a stretcher on their index visit. Exposure Caregiver burden, as collected using the brief twelve-item Quebec French version of the Zarit Brief Burden Interview (ZBI). Main Outcomes and Measure Revisits to the ED were defined as a return to any ED in the 4-hospital network within 3, 7, or 30 days of the index visit. Admissions were return visits to the ED within 30 days resulting in hospitalization. Results Among 1,409 caregiver-patient dyads, ZBI scores averaged 7.33 (SD = 7.11). Most caregivers were women (69%). Caregivers were most often spouses (48%) of patients or children of patients (38%). Among all patients, 5.3% returned to the ED within 3 days, 9.4% returned within 7 days, 20.7% returned within 30 days and 6.2% were admitted within 30 days. Each point increase on the ZBI scale was associated with a 2.8% increase in the odds of a 30-day revisit to the ED (p = 0.03), but not in models with shorter time windows, nor for admissions. ZBI scores on 30-day ED revisits were moderated by the COVID-19 pandemic waves: the first inter-wave period attenuated the association. Conclusions and Relevance Caregiver burden may modestly predict ED revisits over 30 days. Future studies may enhance the management of ED revisits by predicting the longitudinal impact of caregiver burden on ED use in older adults. Trial Registration https://clinicaltrials.gov/ct2/show/NCT04093245 Key points Question: Can caregiver burden predict emergency department (ED) revisits and admissions within 30 days of discharge among older adults? Findings: In this prospective cohort study, higher caregiver burden was associated with a modest increase in the likelihood of 30-day ED revisits, though not with shorter-term revisits or admissions. Meaning: Reducing caregiver burden may help prevent returns to the ED within 30 days among community-dwelling older adults.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.295
Teacher spread0.276 · 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
Published2024
Admission routes3
Has abstractyes

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