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Record W1579592058

Abstract 338: Determining the Timeliness of Key Resuscitative Interventions Among Pediatric Patients Treated for Septic Shock at a Pediatric Tertiary Care Hospital

2014· article· en· W1579592058 on OpenAlexaff
Blair L. Bigham, Emilio Aguirre, Karen Choong, Melissa Parker

Bibliographic record

VenueCirculation · 2014
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineSeptic shockEmergency medicineBolus (digestion)Psychological interventionVasoactiveTertiary careEarly goal-directed therapySepsisEmergency departmentIntensive careRetrospective cohort studyPediatric intensive care unitIntensive care unitResuscitationIntensive care medicinePediatricsSevere sepsisInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Severe sepsis and septic shock (SS) are associated with high mortality and morbidity, and interventions are time-sensitive. Adherence to evidence based guidelines is poor. Objective: To evaluate the time required to deliver components of the American College of Critical Care Medicine guidelines for SS; vascular access, isotonic fluid bolus of 20mL/kg within 15 min, antibiotics within 15 min, and vasoactive medications. Method: A retrospective case series to determine when SS criteria were met (Time Zero) and the timing of recommended therapies for children 29 days to 17 yrs with SS admitted to the intensive care unit at a pediatric hospital. Descriptive statistics are reported as median and ranges. Results: Of 498 admissions between 01/06/2010 and 30/06/2011, 28 (6%) met criteria for SS; 13 (46.4%) in the emergency department, 7 (25%) on a ward and 8 (28.6%) in the ICU. Half (14) were males, median age and weight were 4.4yrs (0-17) and 19kg (2.9-85.9) and 4 (14.3%) patients died. Septic shoc...

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.313
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes1
Has abstractyes

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