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Record W2889415516 · doi:10.1097/pec.0000000000001598

Examining the Appropriateness and Motivations Behind Low-Acuity Pediatric Emergency Department Visits

2018· article· en· W2889415516 on OpenAlexaffabout
Maya Haasz, Daniel Ostro, Dennis Scolnik

Bibliographic record

VenuePediatric Emergency Care · 2018
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoYork Central Hospital
Fundersnot available
KeywordsMedicineOvercrowdingTriageEmergency departmentEmergency medicineMedical emergencyPediatric emergency medicineFamily medicinePediatricsEmergency physicianNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: High patient volumes have a deleterious effect on care in the pediatric emergency department (PED). Our study assessed the motivation for PED visits that could have been assessed by a primary care physician. METHODS: We identified a convenience sample of patients presenting to the SickKids Hospital PED in June and July 2011 with a Paediatric Canadian Triage and Acuity Score 4 or 5. Patients completed a forced answer yes/no survey describing potential motivators for visiting the PED. Visit appropriateness was determined by a modified version of the DeAngelis tool, an explicit criteria-based tool frequently used for this purpose. RESULTS: Of the included 635 patients with Paediatric Canadian Triage and Acuity Score 4 and 5, 25% were truly inappropriate as per DeAngelis criteria. Of these, perceived expertise at the tertiary care hospital (93.1%) and ease of getting tests (80.8%) were the most common reasons behind PED presentation. CONCLUSIONS: Patients presenting to our PED typically have primary care physicians; however, access to their physicians during off-hours and availability of off-site testing is limited. Public policy aimed at decreasing overcrowding in the PED should address these themes.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.287
Teacher spread0.262 · 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 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

Citations21
Published2018
Admission routes2
Has abstractyes

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