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Record W1972566009 · doi:10.12927/hcq.2009.20964

Impact of Adverse Events on Hospital Disposition in Community-Dwelling Seniors Admitted to Acute Care

2009· article· en· W1972566009 on OpenAlexafffundabout
Stacy Ackroyd‐Stolarz, Judith Read Guernsey, Neil J. MacKinnon, George Kovács

Bibliographic record

VenueHealthcare Quarterly · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchNova Scotia Health Research Foundation
KeywordsMedicineAcute careRetrospective cohort studyAdverse effectEmergency medicineCohortHealth careCommunity hospitalPopulationEnvironmental healthInternal medicineNursing

Abstract

fetched live from OpenAlex

Older adults (> or =65 years) have been identified as a high-risk group for the occurrence of adverse events (AEs) in hospital. The purpose of this paper is to describe the association between AEs and disposition for a population of hospitalized seniors. All community-dwelling seniors admitted to an acute care in-patient unit were eligible for inclusion in this retrospective cohort study conducted at an adult tertiary care facility in Atlantic Canada between July 1, 2005, and March 31, 2006. AEs were identified from administrative data using validated screening criteria derived from the International Classification of Diseases (ICD) diagnosis and external cause of injury codes. Of the 982 eligible patients, 140 (14%) had evidence of at least one AE. There were 136 in-hospital deaths (14%). There was no significant difference in the proportion of deaths between those who experienced an AE and those who did not. However, of the 29 patients who were discharged to a long-term care facility, a significantly higher proportion had an in-hospital AE (6% versus 2%, p < .009). The potential contribution of an AE to the subsequent placement in a long-term care facility offers a compelling reason to develop prevention strategies for hospitalized seniors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.456
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

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

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 head0.029
GPT teacher head0.423
Teacher spread0.393 · 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 teacher head, 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

Citations16
Published2009
Admission routes3
Has abstractyes

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