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Record W2116552160 · doi:10.1123/jab.2013-0081

Measurement of the Effect of Playground Surface Materials on Hand Impact Forces During Upper Limb Fall Arrests

2014· article· en· W2116552160 on OpenAlexafffund
Woochol Joseph Choi, Harjinder Kaur, Stephen N. Robinovitch

Bibliographic record

VenueJournal of Applied Biomechanics · 2014
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsSimon Fraser University
FundersCanadian Institutes of Health Research
KeywordsNatural rubberTileFiberRADIUSTorsoGeotechnical engineeringMaterials scienceComposite materialGeologyMedicineComputer scienceAnatomy

Abstract

fetched live from OpenAlex

Distal radius fractures are common on playgrounds. Yet current guidelines for the selection of playground surface materials are based only on protection against fall-related head injuries. We conducted "torso release" experiments to determine how common playground surface materials affect impact force applied to the hand during upper limb fall arrests. Trials were acquired for falls onto a rigid surface, and onto five common playground surface materials: engineered wood fiber, gravel, mulch, rubber tile, and sand. Measures were acquired for arm angles of 20 and 40 degrees from the vertical. Playground surface materials influenced the peak resultant and vertical force (P<.001), but not the peak horizontal force (P=.159). When compared with the rigid condition, peak resultant force was reduced 17% by sand (from 1039 to 864 N), 16% by gravel, 7% by mulch, 5% by engineered wood fiber, and 2% by rubber tile. The best performing surface provided only a 17% reduction in peak resultant force. These results help to explain the lack of convincing evidence from clinical studies on the effectiveness of playground surface materials in preventing distal radius fractures during playground falls, and highlight the need to develop playground surface materials that provide improved protection against these injuries.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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)0.0000.000

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 head0.012
GPT teacher head0.280
Teacher spread0.268 · 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 designBench or experimental
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
Published2014
Admission routes2
Has abstractyes

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