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Record W4405118072 · doi:10.21275/sr21421213644

A Study to Find out Effect of Mechanical Knee Traction versus IFT on Pain and Functional Disability in Patients with Knee Osteoarthritis - An Interventional Study

2021· article· en· W4405118072 on OpenAlexaboutno aff
Rajvee Manvar, Kinjal Bagthariya

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2021
Typearticle
Languageen
FieldMedicine
TopicIntramuscular injections and effects
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicineKnee painPhysical therapyPhysical medicine and rehabilitationTraction (geology)Knee flexionAlternative medicineEngineering

Abstract

fetched live from OpenAlex

OA is degenerative joint disorder of articular cartilage leading to a decreased joint space width and range of motion. OA represent a major cause of impairment and disability among the elderly community. Objective of this study to find of comparative effect of mechanical knee traction versus IFT on pain and functional disability in patients with knee arthritis.30 patients with knee arthritis were allocated into 2 groups. Group A was given mechanical knee traction and conventional therapy. Group B was given IFT and conventional therapy. Visual analogue scale (VAS) was used to assess knee pain and western Ontario and McMaster universities osteoarthritis index used to measure physical function (WOMAC). Treatment was given for 7 days. Data was analysed by using SPSS software version 20. Within group there was significant improvement seen by Wilcoxon signed rank test and Between groups no significant difference using mannwhitney U test and VAS (Z=-0.832, p=-.403) and WOMAC (Z=-.727, p=-.467). result of this study says that mechanical knee traction and IFT both are equally effective in reducing pain and improving physical function in patients with knee osteoarthritis.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.113
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
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)0.0000.000

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 head0.041
GPT teacher head0.400
Teacher spread0.360 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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