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Stroke and risk factors for falls in elderly individuals

2017· article· en· W2767370589 on OpenAlexaff
Alice Gabrielle de Sousa Costa, Ana Railka de Souza Oliveira, Thelma Leite de Araújo, Natália Barreto de Castro, Viviane Martins da Silva, Marcos Venícios de Oliveira Lopes

Bibliographic record

VenueRev Rene · 2017
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsBC Studies
Fundersnot available
KeywordsStroke (engine)MedicinePhysical medicine and rehabilitationGerontologyEngineering

Abstract

fetched live from OpenAlex

Objective: to verify and compare the intrinsic and extrinsic risk factors for falls among older community-dwelling people with and without a stroke history. Methods: a case group and three control groups were established and each group had 15 elderly individuals. Results: the statistical associations were: use of antihypertensive medication among the groups with falls, despite the occurrence of a stroke; use of angiotensin-converting enzyme inhibitor for elderly with a stroke who had an event of fall or not; foot alterations between the case group and people without falls or stroke. Gait difficulty and impaired physical mobility were statistically associated between the case group and people without the occurrence of stroke or falls. Conclusion: the stroke is associated with falls and the intrinsic factors presented greater statistical correlations, supporting the hypothesis that many factors influence the occurrence of falls.

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)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.388
Teacher spread0.339 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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