MétaCan
Menu
← Back to cohort
Record W2946191154

PHYSICAL PAIN IN ELDERLY

2019· article· pt· W2946191154 on OpenAlexaboutno aff
João Pedro Arantes da Cunha, Paloma Almeida Kowalski, Emily Ruiz Cavalcante, José Carlos Souza, José Luís Feltrin Oréfice

Bibliographic record

VenueSimpósio Internacional de Neurociências da Grande Dourados · 2019
Typearticle
Languagept
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsAffect (linguistics)Chronic painEpidemiologyMedicinePhysical therapyGerontologyPsychologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Introduction: With aging, there is a reduction in the reserve capacities of the locomotor system by the loss of cells capable of functioning normally. There are neurological changes, as well as in muscles, bones and joints, which affect both the morphological structure and the mobility, consequently, interfering with the activities of daily life. Pain syndromes are one of the main clinical conditions affecting the elderly. Frequent pain, at all stages of life, is important for them as it is in this age group that the frequency of disabling, chronic and / or degenerative diseases increases, which may limit their activities. Material and Methods: An epidemiological, descriptive and cross-sectional study was carried out, approved by the Committee of Ethics and Research with Human Beings of the Catholic University of Don Bosco. Interviews were conducted with 130 elderly people at the Social Service for Commerce (SESC-Horto), in the city of Campo Grande (MS); To locate pain among the participants, a body diagram extracted from the McGill Pain Questionnaire and a scale of faces for measuring pain severity were utilized. A sociodemographic questionnaire and a Free and Clarified Consent Form were also applied. Results: The average age of 71.6 years, with a minimum age of 60 and a maximum of 88 were found in the analysis of the results, as well as a majority of women, representing 106 of the participants, corresponding to 81% of the sample. Regarding the results found in the variable intensity of pain, 27% of the participants presented the intensity of pain that hurts a little more, marked according to the scale of faces with the caricature number 2, followed by pain that hurts a lot, depicted by caricature number 3 in the scale of faces, with 24% of participants. Concerning pain localization results, more than half of the participants reported knee pain, followed by complaints in the lower back and hip and thigh. It is noteworthy that some participants reported experiencing pain in more than one body segment. Discussion and Conclusion: The prevalence of pain is shown to be highly predominant in the articular regions, in the evaluated population, whereby the support joints make up a higher rate among the related variables. As for pain intensity, it appears to have a very good rate to be considered, presenting itself as tolerable among the participants of the research, which can assert or at least justify the literary reports that the greater the age, the lower the intensity of the pain is, as well as its correlation with gender.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0060.001

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 head0.011
GPT teacher head0.279
Teacher spread0.268 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueSimpósio Internacional de Neurociências da Grande Dourados→Same topicMusculoskeletal pain and rehabilitation→French-language works237,207→