The usage of analgesics among arthritis patients in Hospital Selayang
Bibliographic record
Abstract
Background: It is found that almost all prescribers prescribe analgesics and Nonsteroidal Anti-inflammatories Drugs (NSAIDs) for arthritis patients, as one of the pain management strategies other than Disease-modifying Anti Rheumatic Drugs (DMARDs). Today, clinical experience portrays that analgesics are not used as recommended. Thus, this study is to determine the patterns of analgesic use and the relationship with pain. Method: The survey was conducted at rheumatology clinic in Hospital Selayang. Patients were selected by using convenient sampling method. The pain score was measured by using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). This followed by the difference in pain between those agreeing and not agreeing with the statements given which were determined by using independent ttest. The relationship between the pain score and the regularity of analgesics use were assessed by using correlation. Results: 130 (100%) completed the study. 34 of the respondents (26.2%) reported of having no pain, 26 respondents (20.0%) described of having slight pain, 25 respondents (19.2%) having moderate pain, 45 respondents (34.6%) having severe pain and none of them having extreme pain. Descriptive statistics and T-test were used to determine the patterns of analgesic use and the difference in pain between those agreeing and disagreeing with the pain management statements respectively. It found that those having high mean pain score agreed that they took analgesics regularly. Correlation study had been conducted too and it showed that the increase in pain score does affect the increase in regularity of analgesic use with p <0.05. Conclusion: The patterns of analgesic use vary from patients to patients, but, generally we conclude that higher pain intensity results a higher regularity of analgesic use. Pharmacists are encouraged to give appropriate guidelines regarding analgesic use to patients in order to ensure safety use of analgesics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".