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Effectiveness of Epsom Salt with Hot Water Application on Knee Joint Pain among Elderly in a Selected Rural Area at Puducherry

2020· article· en· W3022105324 on OpenAlexaboutno aff
Lavanya Abstract, Lavanya Sankar

Bibliographic record

VenuePondicherry Journal of Nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsKnee JointMedicineSalt waterJoint painKnee painJoint (building)GeographyPhysical therapySurgeryEnvironmental scienceEnvironmental engineeringEngineeringCivil engineeringOsteoarthritisAlternative medicine

Abstract

fetched live from OpenAlex

Osteoarthritis (OA) is a disease of cartilage degradation, which results pain in major joints, especially in knee joint. Globally, OA ranks eighth in all diseases and covers around 15% proportions among all musculoskeletal problems. Background: Osteoarthritis is a disease of the cartilage which leads to degradation and results in pain in the major joints, especially in the knee joints. Knee joint pain is the most frequent complaint among the geriatric population. The objectives of this study were to assess the level of pain in knee joint among elderly, to evaluate the effectiveness of hot water application with Epsom salt on knee joint pain among elderly, and to find out the association between the level of pain in knee joint and selected demographic variables. Materials and methods: A preexperimental research design was adopted for this study. This study was conducted among elderly aged above 60 years residing in T.N. Palayam. In total, 29 samples of elderly aged above 60 years residing in T.N. Palayam were selected based on the purposive sampling technique. The demographic data were collected from the elderly and then the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) oesteoarthritis rating scale was used to assess the degree of pain.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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 designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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