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Record W2524417809 · doi:10.1542/hpeds.2016-0006

A Modified Delphi Study to Identify Factors Associated With Clinical Deterioration in Hospitalized Children

2016· article· en· W2524417809 on OpenAlexaffabout
Kristina Krmpotic, Anna-Theresa Lobos

Bibliographic record

VenueHospital Pediatrics · 2016
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of OttawaJaneway Children's Health and Rehabilitation CentreChildren's Hospital of Eastern OntarioMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineDelphi methodEmergency departmentEarly warning scoreEmergency medicineProspective cohort studyVital signsMEDLINEWarning signsSeverity of illnessMedical emergencyPediatricsFamily medicineIntensive care medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Hospitalized children who are admitted to the inpatient ward can deteriorate and require unplanned transfer to the PICU. Studies designed to validate early warning scoring systems have focused mainly on abnormalities in vital signs in patients admitted to the inpatient ward. The objective of this study was to determine the patient and system factors that experienced clinicians think are associated with progression to critical illness in hospitalized children. METHODS: We conducted a modified Delphi study with 3 iterations, administered electronically. The expert panel consisted of 11 physician and nonphysician health care providers from hospitals in Canada and the United States. RESULTS: Consensus was reached that 21 of the 57 factors presented are associated with clinical deterioration in hospitalized children. The final list of variables includes patient characteristics, signs and symptoms in the emergency department, emergency department management, and system factors. CONCLUSIONS: We generated a list of variables that can be used in future prospective studies to determine if they are predictors of clinical deterioration on the inpatient ward.

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)0.001
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 score0.008
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.095
GPT teacher head0.392
Teacher spread0.297 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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