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Record W1849246617 · doi:10.5858/2005-129-205-aodami

Association of drowning and myocarditis in a pediatric population: an autopsy-based study.

2005· article· en· W1849246617 on OpenAlexaff
Gino R. Somers, Charles R. Smith, Gregory J. Wilson, Maria Zieleńska, Raymond Tellier, Glenn Taylor

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsSickKids Foundation
Fundersnot available
KeywordsMyocarditisMedicineAutopsyAccidentalPediatricsPoison controlPopulationContext (archaeology)Internal medicineMedical emergency

Abstract

fetched live from OpenAlex

CONTEXT: Drowning is a frequent cause of accidental death in childhood, but the association of myocarditis and drowning has only rarely been reported. OBJECTIVE: To report 5 cases of drowning in children with coexistent myocarditis. DESIGN: A retrospective review of autopsy records of patients 0 years to 18 years of age was performed during a 6-year period (1998-2003, total cases reviewed = 1431). RESULTS: Twenty-two drownings were identified, in 14 male and 8 female children. Five patients (23%), 3 female and 2 male children, had coexistent myocarditis. The 5 patients ranged in age from 23 months to 13 years (mean, 7 years 2 months). None of the patients had antecedent symptomatology suggestive of myocarditis. In all patients, the myocarditis was focal mild or moderate, and the inflammatory infiltrate comprised lymphocytes with smaller numbers of neutrophils. All 5 patients had foci of myocyte necrosis. One patient had histologic evidence of myocardial hypertrophy but no evidence of a cardiomyopathy. Microbiologic studies, including culture, immunohistochemistry, polymerase chain reaction, and reverse transcriptase polymerase chain reaction, revealed Mycoplasma pneumoniae DNA in 1 case. CONCLUSIONS: The finding of myocarditis in a significant proportion of drowning victims in this series highlights the importance of a thorough autopsy examination in apparently straightforward cases and has clinicopathologic significance.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.020
GPT teacher head0.287
Teacher spread0.266 · 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.

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

Citations7
Published2005
Admission routes1
Has abstractyes

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