PHYSICIAN KNOWLEDGE, ATTITUDE AND PRACTICE TOWARDS FIBROMYALGIAAND FACTOR'S INFLUENCING KNOWLEDGE IN EASTERN PROVINCE , SAUDI ARABIA
Bibliographic record
Abstract
Introduction: Fibromyalgia is a common rheumatological disease that is difcult to diagnose , because of it's subjective symptoms and decient physician's knowledge of the disease . The purpose of this study is to assess knowledge, attitude and practice of family physicians regarding bromyalgia Methods: The study was a cross sectional with an online survey that was administered to general practitioners and family physicians in Eastern province, responses were obtained from 209 participants, knowledge about bromyalgia, attitude and practice were assessed Results: Regarding knowledge about bromyalgia, 89% of the respondents had poor knowledge. Attitudes revealed that More than half of the respondents (56%) thought that bromyalgia is easy to diagnosed. However, only 12.4% of the medical practitioners agreed that diagnosis of bromyalgia can be conducted in PHC setting and only 16.7% felt condent to use (ACR) criteria. Regarding management, about 27% believed they can manage bromyalgia . Only a quarter (24.9%) said they never manage any cases of bromyalgia. Conclusions: knowledge, attitude and practice regarding bromyalgia are poor which results in delay in the diagnosis and management.To overcome this delays in diagnosis and treatment and improve knowledge about bromyalgia, further education is needed
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".