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Record W2755047203 · doi:10.3233/bmr-169718

PNF and manual therapy treatment results of patients with cervical spine osteoarthritis

2017· article· en· W2755047203 on OpenAlexaboutno aff
Tomasz Maicki, Jan Bilski, Elżbieta Szczygieł, Rafał Trąbka

Bibliographic record

VenueJournal of Back and Musculoskeletal Rehabilitation · 2017
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical therapyManual therapyOsteoarthritisCervical spinePhysical medicine and rehabilitationSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to evaluate the effectiveness of PNF and manual therapy methods in the treatment of patients with cervical spine osteoarthritis, especially their efficacy in reducing pain and improving functionality in everyday life. Long-term results were also compared in order to determine which method of treatment is more effective. SUBJECTS AND METHODS: Eighty randomly selected females aged 45-65 were included in the study. They were randomly divided into two groups of 40 persons. One group received PNF treatment and the other received manual therapy (MAN.T). To evaluate functional capabilities, the Functional Rating Index was used. To evaluate changes in pain, a shortened version of the McGill Questionnaire was used. RESULT: The PNF group achieved a greater reduction in pain than the MAN.T group. The PNF group showed a greater improvement in performing daily activities such as sleeping, personal care, travelling, work, recreation, lifting, walking and standing as well as decreased intensity and frequency of pain compared to the MAN.T group. CONCLUSION: The PNF method proved to be more effective in both short (after two weeks) and long (after three months) term.

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)0.001
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 score0.616
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.008
GPT teacher head0.283
Teacher spread0.275 · 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

Citations33
Published2017
Admission routes1
Has abstractyes

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