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Record W3126743420 · doi:10.13702/j.1000-0607.200260

[Therapeutic effect and mechanism of silver needle heat conduction therapy combined with loxoprofen sodium patch in patients with knee osteoarthritis].

2021· article· en· W3126743420 on OpenAlexaboutno aff
Zhenwu Zhang, Di-Yang Ling, Jian Han, Chengxiang Wu, Zheyin Wang

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACMedicineOsteoarthritisInternal medicineAlkaline phosphataseUrologySodiumGastroenterologyPharmacologyPathologyChemistryBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: To observe the clinical efficacy of silver needle heat conduction therapy combined with loxoprofen sodium patch in the treatment of knee osteoarthritis (KOA). METHODS: A total of ninety-two patients with KOA were randomly and equally divided into loxoprofen sodium group and silver needle heat conduction therapy + loxoprofen sodium (combination) group, with 46 cases in each group. Patients of the combination group were treated with silver needle heat conduction therapy combined with loxoprofen sodium patch, while those of the loxoprofen sodium group were treated with loxoprofen sodium patch. The treatment was conducted for 4 weeks. The Western Ontario McMaster Universities Osteoarthritis Index (WOMAC), bone metabolism index [including bone gla protein (BGP), bone-specific alkaline phosphatase (BALP), tartrate resistant acid phosphatase isomer (TRACP)-5b], and inflammation factors [including the tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), interleukin-1β (IL-1β)] were observed before and after treatment. The therapeutic effect was assessed after the treatment. RESULTS: <0.05). CONCLUSION: Silver needle heat conduction therapy combined with loxoprofen sodium can effectively treat KOA, its mechanism may be related to alleviating inflammation and improving bone metabolism.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.023
GPT teacher head0.280
Teacher spread0.258 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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