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Record W2765519032 · doi:10.24095/hpcdp.37.10.07

At-a-glance - Sentinel surveillance of emergency department presentations for barbecue brush-related injuries: the electronic Canadian Hospitals Injury Reporting and Prevention Program, 2011 to 2017

2017· article· en· W2765519032 on OpenAlexaffvenueabout
Deepa P. Rao, T. Minh, Jennifer Crain, Steven McFaull, Rebecca Stranberg, T Mersereau, Wendy Thompson

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2017
Typearticle
Languageen
FieldMedicine
TopicForeign Body Medical Cases
Canadian institutionsHealth CanadaPublic Health OntarioUniversity of TorontoPublic Health Agency of Canada
Fundersnot available
KeywordsInjury surveillanceBrushMedicineBristleMedical emergencyEmergency departmentPoison controlInjury preventionEngineering

Abstract

fetched live from OpenAlex

A barbecue (BBQ) brush is a common household item designed for cleaning grills used for barbecuing. Data from the electronic Canadian Hospitals Injury Reporting and Prevention Program database were analysed to estimate the frequency of injuries related to BBQ brushes as a proportion of all injuries, as well as to describe characteristics associated with such injury events. Between April 1, 2011 and July 17, 2017, BBQ brush injuries were observed at a frequency of 1.5 cases per 100 000 eCHIRPP cases (N = 12). Findings suggest that in addition to risks associated with the ingestion of loose BBQ brush bristles attached to foods, loose bristles could also result in injury via other mechanisms.

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)0.001
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 score0.015
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.387
Teacher spread0.351 · 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

Citations3
Published2017
Admission routes3
Has abstractyes

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