Evaluation of the effectiveness of Therapeutic Laser and TENS in the reduction of pain in patients with low back pain
Bibliographic record
Abstract
Chagas Júnior, RAQ. Evaluation of the effectiveness of Therapeutic Laser and TENS in the reduction of pain in patients with low back pain. [Thesis]. Araçatuba: São Paulo State University (Unesp), School of Dentistry; 2016. Introduction: Low back pain is often accompanied by exacerbation of pain and decreased functional capacity. Many non-pharmacological therapies such as laser and TENS are indicated for its treatment, but their effects are not fully understood, nor is the minimum number of sessions for therapeutic effect. Objective: To analyze the effect of different therapeutic modalities (laser and TENS) non-relief of chronic non-specific chronic pain by varying the number of clinical sessions. Methods: The sample consisted of 30 patients randomly selected and divided into 2 groups treated by two therapeutic modalities: G1 - laser (n = 15), G2 - TENS (n = 15). All patients were assessed before and after treatment, by the McGill Pain Questionnaire (MPQ), and Functional Capacity by the Roland Morris Questionnaire. And evaluated daily by Visual Analog Scale (EAV). By this methodology it was possible to conclude that the proposed interventions statistically reduce the short-term pain intensity in patients with low back pain, alter the perception of the pain descriptors only in the Laser Group before and after treatment; Just as they alter the reports of physical incapacity
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".