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A systematic review: The role and impact of the physician assistant in the emergency department

2011· review· en· W1567582091 on OpenAlexaff
Quynh Doan, Vikram Sabhaney, Niranjan Kissoon, Sam Sheps, Joel Singer

Bibliographic record

VenueEmergency Medicine Australasia · 2011
Typereview
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineEmergency departmentFamily medicinePhysician assistantsMEDLINEWork flowMedical emergencyEmergency medicineNursingHealth careNurse practitioners

Abstract

fetched live from OpenAlex

This systematic review describes the role and impact of physician assistants (PAs) in the ED. It includes reports of surveys, retrospective and prospective studies as well as guidelines and reviews. Seven hundred and twelve studies were identified of which only 66 were included, and many of these studies were limited by methodological quality. Generally the use of PAs in the ED is modest with 13-18% of US EDs having PAs although academic medical centres report PA use in 65-68% of EDs. The evidence indicates that PAs are reliable in assessing certain medical complaints and performing procedures, and are well accepted by ED staff and patients alike. There is limited evidence as to whether PAs improve ED flow or are cost-effective. Future studies on work processes, cost-effectiveness, unfamiliar patients' willingness to be treated by non-physician providers, and ED physicians' acceptability of PAs are needed to inform and guide the integration of PAs into EDs.

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)0.015
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0110.013
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.095
GPT teacher head0.483
Teacher spread0.387 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations62
Published2011
Admission routes1
Has abstractyes

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