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Record W4210413087 · doi:10.1111/apa.16281

A ten‐year retrospective case review of risk factors associated with sleep‐related infant deaths

2022· article· en· W4210413087 on OpenAlexaffabout
Maria Paula Godoy, Matthew Maher

Bibliographic record

VenueActa Paediatrica · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsGovernment of ManitobaChildren's Hospital of Winnipeg
Fundersnot available
KeywordsMedicineIndigenousSleep (system call)PediatricsRetrospective cohort studyInfant mortalityMedical recordRisk factorDemographyEnvironmental healthSurgeryPopulation

Abstract

fetched live from OpenAlex

AIM: The study aimed to identify risk factors associated with sleep-related deaths of infants (0-24 months) in the province of Manitoba, Canada, between January 2009 and December 2018. METHODS: A systematic retrospective case review of autopsies and administrative records in Manitoba between 2009 and 2018. RESULTS: A total of 145 infants died in cases where unsafe sleep environments were known to have contributed to or resulted in their death and where no explained medical causes were identified. Where data complete, all infants had at least one known risk factor for sleep-related deaths, and 96% had multiple. The most common risk factors increased over time and included objects in the sleeping environment (90% of cases), not approved sleep surfaces (77%) and bedsharing (50%). Indigenous infants, infants of young mothers and infants in low-income neighbourhoods are overrepresented. Risk factors for Indigenous infants differed from cases involving non-Indigenous infants. CONCLUSION: A high proportion of sleep-related infant deaths were associated with not approved sleep surfaces and bedsharing, especially for infants under one year. Families in low-income neighbourhoods, Indigenous families and families with young mothers were disproportionately affected by sleep-related infant deaths. There is a need to enhance messaging and smoking cessation messaging in Indigenous communities to prevent sleep-related deaths.

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)0.000
metaresearch head score (Gemma)0.006
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 score0.119
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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