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Record W628540872 · doi:10.1155/2015/857890

Improving Health Care Efficiency Through the Integration of a Physician Assistant into an Infectious Diseases Consult Service at a Large Urban Community Hospital

2015· article· en· W628540872 on OpenAlexaffabout
Melissa Decloe, Janine McCready, James M. Downey, Jeff Powis

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2015
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsToronto General HospitalUniversity of TorontoMcMaster UniversityToronto East General HospitalHamilton General Hospital
Fundersnot available
KeywordsMedicineHealth careFamily medicineDemographyGerontologyEmergency medicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Physician assistants (PAs) have recently been introduced into the Canadian health care system in some provinces; however, there are little data demonstrating their impact. METHODS: A retrospective case-control study was conducted between January 2010 and December 2013. Length of stay (LOS) and mortality were examined in the infectious diseases consult service (IDCS) compared with hospital-wide controls. The two-year period before the introduction of the PA to the IDCS of a large urban community hospital in Canada (2010 to 2011) was compared with the two-year period following the introduction of the PA (2012 to 2013). RESULTS: Following the introduction of a PA to the IDCS, there was a decrease in time to consultation from 21.4 h to 14.3 h (P<0.0001). LOS was significantly decreased among IDCS patients by 3.6 days more than that seen in matched hospital-wide controls (P=0.0001). Mortality did not significantly change after PA introduction in either cases or controls. DISCUSSION/CONCLUSION: PAs can improve health efficiencies in the Canadian health care setting, leading to reduction in LOS.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.018
GPT teacher head0.346
Teacher spread0.328 · 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.

Study designQualitative
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
Published2015
Admission routes2
Has abstractyes

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