APLICACÃO DE NEUROESTIMULAÇÃO ELÉTRICA TRANSCUTÂNEA (TENS) COMO TÉCNICA DE ANALGESIA DURANTE FISIOTERAPIA PÓS-CIRÚRGICA EM PACIENTES COM ARTROPLASTIA TOTAL DE QUADRIL
Bibliographic record
Abstract
Total Hip Arthroplasty (THA) is a common procedure in the present time, especially due to the raise of the population's life expectancy. Elderly patients are great candidates to this surgical procedure and also very vulnerable, in relation to the surgery risks themselves, just like the action of the medicine in the post-operatory. Non-pharmacological resources such as the Transcutaneous Electrical Nerve Stimulation (TENS) are being greatly studied and used by physiotherapy in post-surgical patients. This research's main objective was to verify the influence of the TENS as coadjutant in the cemented THA post-operatory pain relief. The sample (n=23) was consisted of patients in the first post-operatory day of hip arthroplasty, between 60-80 years old, that were divided into two groups, the control group (n=11) received placebo treatment and the treatment group (n=12) received effective electric analgesia. Both groups received the same medicinal therapy, health care routine and physiotherapeutic protocol. The pain was measured through the visual analog pain scale and the McGill pain questionnaire in 3 steps (in the first and second post-operatory days); before the TENS application, immediately after the application of this resource, and after the physiotherapy (made after the TENS). As an indirect way of evaluating the analgesia, it was also registered the quantity of demanded analgesic medicine of each group. The results show that the treatment group showed enhancement of the pain levels (p>0,05), just as less necessity of analgesic medicine administration, what allows to conclude that the TENS is an efficient coadjutant resource in the post-operatory analgesia during the physiotherapy in elders submitted to THA.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".