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Record W7100057705

For Canadian infants, 1 hospitalization in 20 is injury related, and the rate increases to

2016· article· en· W7100057705 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBryophyte Studies and Records
Canadian institutionsnot available
Fundersnot available
KeywordsInjury preventionOccupational safety and healthPopulationSuicide preventionHealth carePoison controlMarital statusPersonal injury
DOInot available

Abstract

fetched live from OpenAlex

year olds. For 10-14 year olds, injuries are the leading cause of hospitalization; almost 1 in 4 hospitalizations are injury related. According to Canada’s 1993 General Social Survey, 11 % of children aged 0-15 years sustained an injury severe enough to require a visit to the doctor. In contrast to our knowledge of injury and hospital use in children, we know relatively little about vis-its to other medical practitioners. Much of our current knowledge of the medical services used to treat injuries at the population level comes from hospital data (e.g., provincial hospital databases, Canadian Institute for Health Information; Ontario Health Survey). We know less about patterns of health service utilization associated with those children who suffer injuries who are cared for in the home and are not seen at the hospital. The impact of injury includes costs and services beyond hospital fees, particularly for those recovering from injury and requiring on-going treatment from health care providers. Furthermore, children who have a history of injury experiences are more likely to encounter frequent injuries as well as more numerous future injuries requiring medical care.1 This study examines associations between maternal reports of childhood injuries during the last 12 months and vis-its to medical practitioners by age group and gender of the child. In our models we include factors that have been shown to influence both injuries and health service use including gender of the child as well as family socio-demographic indicators such as marital status, household income, house-hold size, and maternal levels of education.2

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.005
GPT teacher head0.191
Teacher spread0.185 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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