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Record W4400306024 · doi:10.47070/ijapr.v12i5.3258

The Efficacy of Wetcupping in Kateegraha (Low Back Pain)

2024· article· en· W4400306024 on OpenAlexaboutno aff
M Nimya, Ambili Krishna, Manju. P.S

Bibliographic record

VenueInternational Journal of Ayurveda and Pharma Research · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLow back painTraditional medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

Among musculoskeletal disorders, low back pain (LBP) has the highest prevalence worldwide and is the primary cause of disability. This is the condition that has the highest potential for rehabilitation benefits Chronic LBP is a major cause of work loss and participation restriction and reduced quality of life around the world. In Ayurveda low back pain is correlated with Kateegraha. Shringa avcharana is one of the methods of Raktamokshana and this can be correlated with Chinese cupping method. The suction through specific cupped instrument was used since prehistoric time for the treatment of disease Objective: This case series aims to show how wet cupping affects lower back pain (Kateegraha). Intervention: five patients who complained of low back pain were included to receive four sittings of wet cupping. Cups were applied on the lumbosacral region once every 7 days for 21 days. The visual analogue scale (VAS) and The Quebec Back Pain Disability Scale was used to evaluate the pain on 7 days, 14 days, and 21 days. Conclusion: It's been demonstrated that wet cupping works well for relieving Kateegraha (low back pain). So, we can conclude that for individuals with low back pain, wet cupping is a successful, safe, practical, and reasonably priced treatment plan.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.058
GPT teacher head0.450
Teacher spread0.392 · 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
Published2024
Admission routes1
Has abstractyes

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