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Record W2028251136 · doi:10.1097/pec.0b013e318155ade4

Retrospective Comparison of Emergency Department Length of Stay for Procedural Sedation and Analgesia by Nurse Practitioners and Physicians

2007· article· en· W2028251136 on OpenAlexaff
Charene Wood, Colleen Hurley, Julie Wettlaufer, Michelle Penque, Steven H. Shaha

Bibliographic record

VenuePediatric Emergency Care · 2007
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSedationEmergency departmentRetrospective cohort studyMedical diagnosisComplicationAirwayTriageEmergency medicineAnesthesiaSurgeryNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine if use of nurse practitioners (NPs) for procedural sedation and analgesia (PSA) compared with physicians (MDs) decreased overall length of stay (LOS) in the pediatric emergency department (PED). METHODS: Retrospective chart review was conducted on all children (age <21 years) undergoing procedural sedation and analgesia (PSA) for 36 consecutive months at a tertiary academic children's hospital (n = 690). Data included times values for triage, evaluation by practitioner (NP, MD), sedation, discharge, and total LOS in the PED. Data collected also included medications given, patient diagnosis, and severe airway complications. RESULTS: Results revealed statistically significant time-related advantages to NP-managed sedations. Both PED LOS and time to sedation were significantly lower for NPs versus MDs across diagnoses (P < 0.01). The diagnoses managed by MDs versus NPs were significantly different for 3 diagnoses: fracture, finger, and lacerations. There were no differences between NP and MD for severe airway complication rates. CONCLUSIONS: Overall LOS and time to sedation were significantly improved when NPs independently managed patients requiring PSA without an increase in documented severe airway complication rates.

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 score0.040
Threshold uncertainty score0.784

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.331
Teacher spread0.318 · 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

Citations14
Published2007
Admission routes1
Has abstractyes

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