MétaCan
Menu
Back to cohort

Fall-related deaths among older adults in British Columbia: cause and effect of policy change

2019· article· en· W2971215136 on OpenAlexafffundabout
Aayushi Joshi, Fahra Rajabali, Kate Turcotte, Marie Denise Beaton, Ian Pike

Bibliographic record

VenueInjury Prevention · 2019
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of British Columbia HospitalBC Centre for Disease ControlProvincial Health Services AuthorityBC Children's Hospital
FundersUniversity of British Columbia
KeywordsAccidentalMedicineDemographyInjury preventionOccupational safety and healthGerontologySuicide preventionPoison controlAccidental fallMedical emergencyEnvironmental healthSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The British Columbia Coroners Service implemented a policy in 2010 advising the reclassification of underlying causes of deaths due to falls from 'natural' to 'accidental'. This study investigates whether observed data trends reflect this change in practice, are artefacts of inconsistent reporting, or indicate a true increase in fall-related deaths. METHODS: Mortality data were analysed from 2004 to 2017 for cases with International Statistical Classification of Diseases and Related Health Problems, 10th Revision fall codes W00-W19, occurring among adults aged 60 years and older. RESULTS: From 2010 to 2012, accidental fall-related deaths increased among those aged 80 years and older, followed by an increase in natural deaths with fall as the contributing cause. CONCLUSIONS: Changes in reporting resulting from the 2010 policy change were observed; however, post-2012 data indicate a reversion to previous reporting practices.

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 score0.048
Threshold uncertainty score0.990

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 integrity0.0000.000
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 head0.009
GPT teacher head0.300
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.

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

Citations10
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueInjury PreventionSame topicInjury Epidemiology and PreventionFrench-language works237,207