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Record W3033238378 · doi:10.1097/pec.0000000000002132

Serial Clinical Scoring to Assess Transported Pediatric Patients

2020· article· en· W3033238378 on OpenAlexaff

Bibliographic record

VenuePediatric Emergency Care · 2020
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsProspective cohort studyIntensive careTrap (plumbing)Scoring systemMEDLINESeverity of illness

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this study was to evaluate serial Transport Risk Assessment in Pediatrics (TRAP) scoring during pediatric critical care transport as a potential measure for specialized pediatric transport teams (PTTs). METHODS: This was a retrospective study with a provincial PTT from a tertiary hospital pediatric intensive care unit. All acutely ill children who were transported by the PTT between 2018 and 2019 were included in the study. The TRAP scores were measured at time of transport team arrival (TRAP1), time at arrival to tertiary center (TRAP2), and 4 hours postarrival to tertiary center (TRAP3). RESULTS: A total of 300 transports were included. Patients' mean age was 54 months, with lower respiratory tract infection (40.7%) as the most common diagnosis. There were significant differences between TRAP1-TRAP2 (P < 0.01) and TRAP1-TRAP3 (P < 0.01), but not between TRAP2-TRAP3 (P = 0.67). The most significant improvements of ΔTRAP1-TRAP2 scores were seen in septic shock (mean, 2.0; SD, 1.7). CONCLUSIONS: The TRAP scores improved following the PTTs' arrival to acutely ill children, particularly with sepsis. Serial TRAP scoring may present a system for evaluation of team performance and/or characterize disease states that are positively impacted by PTTs. Future prospective evaluation is needed to validate TRAP for this purpose.

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 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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.362
Teacher spread0.259 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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