MétaCan
Menu
Back to cohort

Pediatric Drowning

2006· article· en· W2039726769 on OpenAlexaff
Gino R. Somers, David A. Chiasson, Charles R. Smith

Bibliographic record

VenueAmerican Journal of Forensic Medicine & Pathology · 2006
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsAutopsyMedicineIncidence (geometry)ResuscitationNear DrowningOccultPoison controlPleural effusionInjury preventionPediatricsSurgeryInternal medicinePathologyEmergency medicine

Abstract

fetched live from OpenAlex

The pathologic findings in autopsies of drowning victims are nonspecific and vary from case to case. However, most reported pathologic series of drowning cases exclude children and do not take into consideration the unique circumstances surrounding bathtub drownings. In addition, the effect of resuscitation on the autopsy findings has not been studied in children. A retrospective review of autopsy records of non-bathtub drownings from a 20-year period (1984-2003) was performed and 63 cases were identified in 45 males and 18 females (age range 9 months to 17 years). The incidence of frothy exudate, pleural effusion, and increased lung weight was 43%, 36%, and 80%, respectively. The incidence of frothy exudate and the combination of all 3 factors was significantly higher in cases with no resuscitation compared with those cases with attempted resuscitation with or without delayed death. As the interval between the drowning episode and autopsy increased, the incidence of frothy exudate decreased significantly. There was no relationship between these findings and the age and sex of the decedent. Other clinical conditions or occult pathologic findings that may have contributed to death were found in 8 cases (13%). The findings highlight the need for thorough clinicopathologic correlation in cases of drowning to accurately interpret the pathologic findings.

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)0.001
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 score0.161
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.304
Teacher spread0.292 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Forensic Medicine & PathologySame topicInjury Epidemiology and PreventionFrench-language works237,207