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Record W2803825337 · doi:10.1093/pch/pxy054.132

CHARACTERIZATION AND RISK FACTOR IDENTIFICATION IN CHILDREN WITH SEVERE CROUP

2018· article· en· W2803825337 on OpenAlexaff
Natan Bensoussan, Lily H. P. Nguyen, Marcus Oosenbrug, Haitian He, Mélanie Duval

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldMedicine
TopicOtolaryngology and Infectious Diseases
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsCroupMedicineIntubationRetrospective cohort studyRisk factorPediatricsIntensive care medicineCohortInternal medicineSurgery

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Croup is a self-limiting illness predominantly affecting young children, the most common etiology being Parainfluenza virus types 1 and 2. While the majority of croup remains self-limiting, 1.6–3% of cases are severe requiring acute airway management such as intubation and ventilatory support. While much is known about the diagnosis, management, and clinical course of self-limiting croup, factors contributing to severe cases remain unexplored. OBJECTIVES To characterize the clinical features and risk factors associated with severe croup in children admitted to a tertiary care centre. DESIGN/METHODS A retrospective chart review study of paediatric patients with severe croup presenting at a single tertiary care paediatric institution between 2011 and 2015 was performed. Severe croup was defined as those requiring major airway intervention (i.e. intubation), emergent management in the operating room (i.e. rigid bronchoscopy) or admission to the paediatric intensive care unit. Our findings were compared to previous reported characteristics of children with viral croup and recurrent croup. Multiple univariate regression models were constructed to isolate predictor variables of longer hospital stay and isolate confounders such as known comorbidities and risk factors. RESULTS Sixty-seven croup patients were analyzed, aged 4–170 months (median, 15) of which 76.8% were male. Several presented with risk factors, namely previous croup history (23.2%), intubation history (13%), airway abnormalities (8.7%) and nonspecific reactive airway disease (8.7%), while others exhibited comorbid conditions such as asthma (7.3%), GERD (14.5%) and known drug allergy (5.8%). In our cohort, Parainfluenza virus type 3 (PIV3) was shown to be a predictor for increased length of stay with a univariate regression coefficient of 3.2 (95% CI 0.72–5.7). Known croup risk factors did not contribute to the effect observed with PIV3 (coefficient 3.0, 95% CI 0.4–5.7). Further analysis accounting for comorbidity confounders predicted a coefficient of 2.6 for PIV3 (95% CI 0.01–5.12). Mean length of stay for PIV3 infected children was 7.4 nights. CONCLUSION To our knowledge, this is the first study linking PIV3 with severe croup and predicting longer hospitalizations in this cohort. Further investigation is required to determine optimal management of PIV3-infected croup patients to shorten clinical course and improve patient outcomes.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.007
GPT teacher head0.251
Teacher spread0.245 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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