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Record W3197010566 · doi:10.37506/ijfmt.v15i4.16946

Self-management of Elderly Patients with Osteoarthritic Knee on Recovery Outcomes

2021· article· en· W3197010566 on OpenAlexaboutno aff
Magda M. Mohsen, Nabila Sabola, Nagwa Ibrahim El-khayat, Entsar A. Abd El-Salam

Bibliographic record

VenueIndian Journal of Forensic Medicine & Toxicology · 2021
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicineWOMACPhysical therapyKnee painInclusion and exclusion criteriaOutpatient clinicSelf-managementArthritisElderly peopleIntervention (counseling)GerontologyAlternative medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Background: Knee osteoarthritis accounts for almost four fifths of the burden of worldwide. It is the leadingcause of mobility impairment, disability and loss of function in older adults. This study aimed to examine theeffectiveness of self-management of elderly patients with osteoarthritic knee on recovery outcomes.Material and methods: A quasi- experimental design was utilized to conduct this study. The study wascarried out at outpatient clinic of Shebin- Elkom University and Educational Hospital, and then they werefollowing up at their homes. 100 elderly patients were selected who met inclusion and exclusion criteria.Aconstructed interviewing questionnaire, arthritis self-efficacy scale, and Western Ontario and McMasterUniversities Osteoarthritis (WOMAC) index were used to collect the data.Results: there was increase in the mean total pain self-efficacy score, and other symptoms self-efficacyscore in study group than control group.There was decrease in the mean total physical function WOMACscore in study group than control group after intervention.Conclusion: Implementation of self-management for elderly patients was effective in managementsymptoms of knee osteoarthritis among study group compared to control group.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.009
GPT teacher head0.243
Teacher spread0.235 · 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 designOther design
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIndian Journal of Forensic Medicine & ToxicologySame topicOsteoarthritis Treatment and MechanismsFrench-language works237,207