FİZİK TEDAVİ UYGULANAN KRONİK BEL AĞRILI YAŞLILARDA BALNEOTERAPİNİN TEDAVİYE ETKİSİ: PİLOT ÇALIŞMA
Bibliographic record
Abstract
<!--block-->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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".