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Record W2755942222 · doi:10.1097/pcc.0000000000001336

Time to Asthma-Related Readmission in Children Admitted to the ICU for Asthma*

2017· article· en· W2755942222 on OpenAlexaffabout
Sze Man Tse, Christian Samson

Bibliographic record

VenuePediatric Critical Care Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of British ColumbiaUniversité de Montréal
Fundersnot available
KeywordsMedicineAsthmaHazard ratioPediatricsPsychological interventionEmergency medicineRetrospective cohort studyCohortIntensive careCohort studyIntensive care medicineConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare the time to asthma-related readmissions between children with a previous ICU hospitalization for asthma and those with a non-ICU hospitalization and to explore predictors of time to readmission in children admitted to the ICU. DESIGN: Retrospective cohort study using a pan-Canadian administrative inpatient database from April 1, 2008, to March 31, 2014. SETTING: All adult and pediatric Canadian hospitals. SUBJECTS: Children 2-17 years old with a hospitalization for asthma. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 26,168 children were hospitalized 33,304 times during the study period. The time to readmission was shorter in the ICU group compared with the non-ICU group (median time to readmission 27 mo in ICU vs 35 mo in non-ICU group). Preschool-aged children (hazard ratio, 1.48; 95% CI, 1.02-2.14) and increased length of stay (hazard ratio, 1.63; 95% CI, 1.17-2.27) were associated with a shorter time to readmission. CONCLUSIONS: Children previously admitted to the ICU for asthma had a shorter time to asthma-related readmission, compared with children who did not require intensive care, underlining the importance of targeted long-term postdischarge follow-up of these children. Children of preschool age and who have a lengthier hospital stay are particularly at risk for future morbidity.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.015
GPT teacher head0.328
Teacher spread0.313 · 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 designNot applicable
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

Citations33
Published2017
Admission routes2
Has abstractyes

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