The Effect of Low-Level Laser on Postoperative Pain After Elective Cesarean Section
Bibliographic record
Abstract
BACKGROUND: Postoperative pain is one of the major concerns about a cesarean in pregnant women that can lead to serious complications and delayed recovery for patients. OBJECTIVES: The objective is to investigate the effect of low power laser on acute pain after elective cesarean. METHODS: In this randomized, double-blind clinical trial, 80 candidates for an elective cesarean were divided randomly into two groups, control and laser. The type of surgery was the same for both groups, which contained the spinal anesthesia technique. At the end of surgery, the surgical incision in patients who were treated with laser, (GaAlAs: 804 nm and GaAlInp: 650 nm) was irradiated by laser. The control group also received laser off by the same method. Patients were monitored for 24 hours to assess the severity of postoperative pain by VAS, the first request for analgesic and the total consumption of analgesic. RESULTS: The results demonstrated significant reduction of pain in the laser group 1, 4, 8, 12, 16, 24 hours after surgery, compared with the control group (P value < 0.05). Additionally, the average of total received analgesic in the group laser was less than the controls (P value = 0.006). The first request for analgesic in the laser received group was significantly longer than the controls (P value = 0.005). CONCLUSIONS: Low power laser therapy is a good method to reduce postoperative pain due to the fact that it is a safe and non-invasive method which is also accepted by patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".