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Record W2932762658

Don't take down the monkey bars: Rapid systematic review of playground-related injuries.

2019· article· en· W2932762658 on OpenAlexaff
Nicolas Bergeron, Catherine Bergeron, Luc Lapointe, Dean Kriellaars, Patrice Aubertin, Brandy Tanenbaum, Richard Fleet

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsNational Circus SchoolSunnybrook Health Science CentreUniversity of ManitobaUniversité Laval
Fundersnot available
KeywordsMedicineCochrane LibraryInjury preventionPoison controlOccupational safety and healthMEDLINERetrospective cohort studyCohort studyHuman factors and ergonomicsPediatricsEmergency medicineFamily medicineSurgeryMeta-analysisPathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To synthesize the available evidence on playground-related injuries and to determine the prevalence of these injuries in pediatric populations. DATA SOURCES: A rapid systematic review was conducted using PubMed, EMBASE, and the Cochrane Library, as well as the gray literature. STUDY SELECTION: The search was limited to studies published between 2012 and 2016 and identified a total of 858 articles, of which 22 met our inclusion criteria: original quantitative studies published in peer-reviewed journals in the past 5 years, concerning unintentional injuries in playgrounds in children aged 0 to 18 years. SYNTHESIS: Information was collected on study and injury characteristics, and the proportion of pediatric injuries related to playground activity was determined. Studies were performed in various countries and most were retrospective cohort studies. The prevalence of playground-related injury ranged from 2% to 34% (median 10%). Studies varied in the types of injuries investigated, including head injuries, genitourinary injuries, ocular and dental trauma, and various types of fractures. Most injuries were low severity. CONCLUSION: Although playgrounds are a common location where pediatric injuries occur, these injuries are relatively low in frequency and severity.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.015
GPT teacher head0.256
Teacher spread0.240 · 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 designSystematic review
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

Citations16
Published2019
Admission routes1
Has abstractyes

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