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Record W4294300861 · doi:10.1097/md.0000000000030225

Acupotomy combined with intra-articular injection of sodium hyaluronate in the treatment of knee osteoarthritis

2022· article· en· W4294300861 on OpenAlexaboutno aff
Ya-peng Chai, Xiaoqiang Han, Cui Junzhi

Bibliographic record

VenueMedicine · 2022
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisWOMACSodium hyaluronateVisual analogue scaleAdverse effectRetrospective cohort studySurgeryPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

This retrospective study aimed to compare the effects of acupotomy combined with intra-articular injection of sodium hyaluronate (IA-SH) for the treatment of knee osteoarthritis (KOA). Eighty electronic medical records of patients with KOA were retrospectively analyzed. The patients were divided into an intervention group (n = 40, acupotomy plus IA-SH) and a control group (n = 40, IA-SH). Outcome measures included the visual analog scale, the Western Ontario and McMaster Universities Arthritis Index (WOMAC), and adverse events. Outcome data were collected and analyzed before and after treatment. The results of this study showed that there was a greater reduction in the visual analog scale (P < .01) and WOMAC scores (pain, P < .01; stiffness, P < .01; function, P < .01; total, P < .01) in the intervention group than in the control group. In addition, there were no significant differences in adverse events between the 2 groups. In this study, the effects of acupotomy plus IA-SH were superior to those of IA-SH alone for the treatment of patients with KOA. Further prospective studies are required to confirm these findings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.235
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations3
Published2022
Admission routes1
Has abstractyes

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