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Influence of foot pain on frailty symptoms in an elderly population: a case-control study

2021· article· en· W3165384647 on OpenAlexaboutno aff
Emmanuel Navarro‐Flores, Ricardo Vallejo, César Calvo‐Lobo, Marta Elena Losa‐Iglesias, Patricia Palomo‐López, Victoria Mazoteras‐Pardo, Carlos Romero‐Morales, Daniel López‐López

Bibliographic record

VenueSao Paulo Medical Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFoot (prosody)Physical therapyPopulationGerontology

Abstract

fetched live from OpenAlex

BACKGROUND: Frailty is a condition that can increase the risk of falls. In addition, foot disorders can negatively influence elderly people, thus affecting their condition of frailty. OBJECTIVE: To determine whether foot pain can influence a greater degree of frailty. DESIGN AND SETTING: Cross-sectional descriptive study conducted at the University of Valencia, Valencia, Spain. METHODS: A sample older than 60 years (n = 52), including 26 healthy subjects and 26 foot pain patients, was recruited. Frailty disability was measured using the 5-Frailty scale and the Edmonton Frailty scale (EFS). RESULTS: There were statistically significant differences in the total EFS score and in most of its subscales, according to the Mann-Whitney U test (P < 0.05). In addition, foot pain patients presented worse scores (higher 5-Frailty scores) than did healthy patients, regarding matched-paired subjects (lower EFS scores). With regard to the rest of the measurements, there were no statistically significant differences (P > 0.05). The highest scores (P < 0.001) were for fatigue on the 5-Frailty scale and the EFS, and for the subscale of independence function in EFS. CONCLUSIONS: These elderly patients presented impairment relating to ambulation and total 5-Frailty score, which seemed to be linked to the presence of frailty syndrome and foot disorders.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.318
Teacher spread0.299 · 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.

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

Citations6
Published2021
Admission routes1
Has abstractyes

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Same venueSao Paulo Medical JournalSame topicFrailty in Older AdultsFrench-language works237,207