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Record W1591813681 · doi:10.3171/2011.11.peds11299

Performance analysis of winter activity protection headgear for young children

2012· article· en· W1591813681 on OpenAlexaffabout
Blaine Hoshizaki, Michael Vassilyadi, Andrew Post, Anna Oeur

Bibliographic record

VenueJournal of Neurosurgery Pediatrics · 2012
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsChildren's Hospital of Eastern OntarioImpactOntario Neurotrauma FoundationUniversity of Ottawa
Fundersnot available
KeywordsMedicineIce hockeyPoison controlInjury preventionOccupational safety and healthPhysical medicine and rehabilitationPhysical therapyMedical emergency

Abstract

fetched live from OpenAlex

OBJECT: The purpose of this study was to evaluate how currently used helmets would perform for winter play activities, such as tobogganing. In Canada and northern parts of the US, the advent of winter is followed by an increase in visits to hospital emergency departments by young children presenting with head injuries resulting from winter activities. Sliding, skating, skiing, and snowboarding all involve risks of head injury from situations such as falling on ice or sliding into stationary objects. This study compared the protective characteristics of helmets used by young children (< 7 years of age) participating in winter recreational activities. METHODS: Ice hockey, alpine ski, and bicycling helmets were impacted at 2.0, 4.0, 6.0, and 8.0 m/second at the front and side impact location by using a monorail drop rig. RESULTS: The results for the front impact showed that the ice hockey helmet protected the child significantly better at 2 and 4 m/second when considering both linear and angular peak acceleration. The bicycle helmet performed significantly better than the other 2 helmets at 8 m/second for the front location and only angularly for the side impacts. CONCLUSIONS: Depending on the impact velocity of the hazard, the type of helmet significantly affected the risk of brain injury.

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)0.001
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 score0.004
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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

Citations18
Published2012
Admission routes2
Has abstractyes

Explore more

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