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Record W2078147136 · doi:10.1136/emermed-2012-201547

Characteristics of femur fractures in ambulatory young children

2012· article· en· W2078147136 on OpenAlexafffund
Louise Capra, Alex V. Levin, Andrew Howard, Michelle Shouldice

Bibliographic record

VenueEmergency Medicine Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicChild Abuse and Related Trauma
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick Children
KeywordsMedicineFemurAmbulatoryOrthodonticsSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine and identify the characteristics and circumstances of femur fractures in ambulatory young children. DESIGN AND SETTING: Retrospective review of 203 ambulatory children, between 1 and 5 years old, presenting with femur fractures to an urban paediatric hospital over a 10-year period. χ(2) And Student's t test were employed for statistical analysis. RESULTS: The mean age was 36.6 months, with 155 (76.2%) being male. The most frequent mechanism of injury was fall from a height (n=62, 30.5%). The highest number of injuries occurred in 2-3-year-olds. The most common history in 1-2-year-olds was stumbling on/over something causing a fall. For 4-5 year olds it was road traffic accidents. Other additional physical findings were infrequent (14.3%) and not suspicious of inflicted injury. Child protective services concluded three of the cases to be likely non-accidental, and four cases were inconclusive but requiring close follow-up. Of these seven children, six occurred in 1-2-year-olds. No distinguishing feature was noted in fracture type or location. CONCLUSIONS: Femur fractures can occur with low velocity injury whether from a short fall or twisting/stumbling injury in young healthy ambulatory children.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.000

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 head0.016
GPT teacher head0.308
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.

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

Citations20
Published2012
Admission routes2
Has abstractyes

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