MétaCan
Menu
Back to cohort
Record W2187650834

Materials of Conferences

2014· article· en· W2187650834 on OpenAlexaboutno aff
Low-Frequency Impulse Therapy

Bibliographic record

VenueCyberLeninK (CyberLeninka) · 2014
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVisual analogue scaleOsteoarthritisUltrasoundPhysical therapySurgeryRadiology
DOInot available

Abstract

fetched live from OpenAlex

. Eхamined 83 patients with gonarthrosis of the I-II stage in age from 35 to 67 years. Patients were divided into 2 groups on con-ducted therapy. In the main group consisted of 45 patients with gonarthrosis of the I-II stage, received at the background of the standard treatment course ultrasound and low-frequency impulse therapy, the comparison group made up of 38 patients, who had received only standard treatment. Assessment of the degree of infl ammation of the joints was carried out with the help of the scale VAS (Visual Analogue Scale), index WOM-AC (Western Ontario and McMaster University Osteoarthritis), ultrasonography, roentgenogra-phy and measurement of the circumference of the knee joints.Of main group patients in the complex with standard treatment was included ultrasound therapy from the apparatus «Sonomed-5» (BOSCH) on the area of the knee joint, with frequency of 1 MHz, with capacity of 0,1-0,2 W/cm2, in the pulse mode, for 5-6 minutes and low-frequency impulse therapy with frequency of 100 Hz, current strength of 1-2 mA, for 5-6 minutes. The course of treatment is 10-12 daily procedures.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.665
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0020.000
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.6650.292

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 head0.033
GPT teacher head0.277
Teacher spread0.244 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCyberLeninK (CyberLeninka)Same topicMedical and Biological SciencesFrench-language works237,207