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Record W2202459163 · doi:10.1177/0898264315599941

Very Frequent Fallers and Future Fall Injury

2015· article· en· W2202459163 on OpenAlexaffabout
Jeffrey W. Poss, John P. Hirdes

Bibliographic record

VenueJournal of Aging and Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineInjury preventionPoison controlOccupational safety and healthEmergency departmentSuicide preventionFall preventionHuman factors and ergonomicsMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the relationship between falls history, especially those with frequent recent falls, and future injurious falls. METHOD: Resident Assessment Instrument for Home Care records of 167,162 home care recipients in Ontario, Canada, were linked to emergency department records recording an injurious fall. Diagnosis codes further informed the nature of the injuries. RESULTS: Persons with a high number of recent falls tended to be younger, and more likely to have Parkinson's or multiple sclerosis. Odds ratios for a future injurious fall, compared with zero recent falls, were as follows: 1.58 (1 fall), 1.91 (2 or 3 falls), 2.54 (4-8 falls), 3.07 (9 or more falls). Injuries among those with multiple recent falls were more likely to be head injuries with an open wound. DISCUSSION: Persons reporting high number of recent falls were at the greatest risk of a future injurious fall and should receive the greatest attention in care planning.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.056
GPT teacher head0.400
Teacher spread0.344 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2015
Admission routes2
Has abstractyes

Explore more

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