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Record W4383905572 · doi:10.3233/bmr-220295

Clinical effect and safety analysis of long-round needle usage in treating cervical spondylotic radiotelegraphy and its effect on pain and functional recovery

2023· article· en· W4383905572 on OpenAlexaboutno aff
Yingmin Liu, Chengbao Feng, Yuyuan Li, Dandan Qie, Bin Xu, Yafei Wen, Su Ma, Wanglin Yu, Zhanqing Xie

Bibliographic record

VenueJournal of Back and Musculoskeletal Rehabilitation · 2023
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)McGill Pain QuestionnaireLife qualitySurgeryPhysical therapyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Long-round needle usage can treat muscular pain, but there is little research on cervical spondylotic radiculopathy (CSR). OBJECTIVE: To explore the efficacy and safety of long-round needle usage in treating CSR. METHODS: Ninety-eight patients with CSR were randomly divided into control and observation groups. They were treated with filiform needles and long-round needles, respectively. The therapeutic effect, safety, inflammatory factors and neck dysfunction index (NDI), McGill pain questionnaire (MPQ) and Generic Quality of Life Inventory-74 (GQOL-74) scores were compared between the two groups. RESULTS: After treatment, the effective rate and safety of the observation group were better than those of the control group. The NDI and MPQ scores in the observation group were significantly lower than those in the control group, and the GQOL-74 score was higher than that in the control group. The level of interleukin-8 in the observation group was significantly lower than that in the control group, and the level of interleukin-10 was significantly higher than that in the control group. CONCLUSIONS: Long-round needle therapy has a good effect on patients with CSR, which can safely improve the quality of life of patients with mild local inflammatory damage.

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)0.006
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 score0.402
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.309
Teacher spread0.298 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Back and Musculoskeletal RehabilitationSame topicCervical and Thoracic MyelopathyFrench-language works237,207