The Role of Acupuncture in Reducing Pain Scale Scoring in Geriatric Patients with Acute Pain: A Literature Review
Bibliographic record
Abstract
Background: Pain is one of the problems commonly found in geriatric population in the world, causing reduction in quality of life and functionality, and increasing socioeconomic burden. The geriatric population are vulnerable to inadequate analgesia, which increase the risk of forming chronic pain, frailty, depression and anxiety, and increased morbidity. Objective: To review several studies that explain the role of acupuncture in reducing pain scale scoring in geriatric patients with acute pain. Method: Literature searching of studies published between January 2011 to June 2020 was done on the Google Scholar and PubMed databases using the keywords “acupuncture,” “manual acupuncture,” “electroacupuncture,” “laserpuncture,” “laser acupuncture,” “ear acupuncture,” “battlefield acupuncture,” “pain,” and “acute pain.” Randomized controlled trials using pain scales as outcome measurement with the population sample having acute pain and using acupuncture modalities as its treatment were included. Non-English studies that cannot be accessed in full text and did not show number of sample, baseline characteristics, and outcome values, and studies with the mean age of the participants below 60 years were excluded. Result: Seven studies were found and analyzed. The pain scale scoring used included Visual Analog Scale (VAS) (n = 4), Numeric Rating Scale (NRS) (n = 1), McGill Pain Questionnaire (MPQ) (n = 2), and Brief Pain Inventory (BPI) (n = 2), with some studies using more than 1 scale. Conclusion: Acupuncture was found to reduce pain scale scoring of VAS, NRS, MPQ, and BPI significantly, whether statistically or clinically.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".