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Record W2346191596 · doi:10.1097/pec.0000000000000796

Telephone Out Patient Score

2016· article· en· W2346191596 on OpenAlexaff
Candice Bjornson, Janielee Williamson, David W. Johnson

Bibliographic record

VenuePediatric Emergency Care · 2016
Typearticle
Languageen
FieldMedicine
TopicOtolaryngology and Infectious Diseases
Canadian institutionsAlberta Children's Hospital
Fundersnot available
KeywordsCroupMedicineEmergency departmentStridorProspective cohort studyPediatricsTelephone interviewCohortCohort studyEmergency medicinePhysical therapyInternal medicinePsychiatrySurgeryAirway

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective was to derive a simple clinical scoring instrument for assessing children with croup by telephone for use in clinical research studies. METHODS: We reviewed published literature on croup scores, surveyed experienced pediatric emergency nurses and physicians, and conducted a prospective cohort study. Score items were derived from published literature and surveys of experienced clinicians. We enrolled children with croup attending an urban pediatric emergency department. Families of children enrolled were contacted daily by telephone and asked standardized questions about their child's clinical symptoms and family functioning. Data from this survey were used to derive the clinical score. RESULTS: We identified 11 unique croup scores from the literature and interviewed 6 experienced clinicians. We enrolled 330 children and achieved complete follow-up for 301. Of the various groupings of items and duration of assessment, the 2-item score (barky cough and stridor) was the simplest and most reliable. Three days of follow-up yielded optimal correlations. CONCLUSIONS: We derived a 2-item Telephone Out Patient score assessed daily for 3 days after an emergency department visit. Validation of this score in a future, independent prospective cohort is needed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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