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Record W2987426470

THE EFFECTS OF BALNEOTHERAPY IN ELDERLY PATIENTS WITH CHRONIC LOW BACK PAIN TREATED WITH PHYSICAL THERAPY: A PILOT STUDY

2019· article· en· W2987426470 on OpenAlexaboutno aff
Kağan Özkuk, Erdal Dilekçi

Bibliographic record

VenueJournal of Istanbul Faculty of Medicine · 2019
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsBalneotherapyMedicinePhysical therapyVisual analogue scaleAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: The aim of this study was to compare whether balneotherapy has a positive effect on the treatment of elderly individuals receiving physical therapy for chronic low back pain (CLBP). Methods: 244 participants were randomly placed into two groups. The first group was treated with physical therapy (PT), the second group was treated with PT and balneotherapy (BT). Assessments were made using the PainVAS, Quebec Back Pain Disability Scale (Quebec), Health Assessment Questionnaire (HAQ) before treatment (T0) and after treatment (T1). Results: In both groups, there was a statistically significantly decrease in terms of pain-VAS, Quebec and HAQ scores (p<0.001). When pain-VAS scores were compared between the two groups, pain-VAS T0 was significantly higher and pain-VAS T1 was significantly lower in the BT+PT group than the PT group (p=0.001). When the HAQ and Quebec values were compared between the groups, the T0 value was similar in the BT+PT and PT groups (HAQ p=0.068, Quebec p=0.495) while the BT+PT group HAQ and QuebecT1 scoreswere significantly lower than the PT group (p<0.001). The BT+PT group change values were significantly higher than the PT group (p<0.001). Conclusion: These results recommend that combining therapies may be more effective in treating CLBP and balneotherapy may increase the effectiveness of the treatment.

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)0.002
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 score0.196
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.363
Teacher spread0.339 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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