Awareness of complex regional pain syndrome among medical professionals
Bibliographic record
Abstract
Abstract Background Complex regional pain syndrome (CRPS) is a chronic inflammatory and neurological condition with a complex broad spectrum of symptoms and requires input from various clinical specialists such as orthopedic surgeons, anesthetists, rheumatologists, and rehabilitation physicians. Aim The aim is to study the awareness of CRPS among medical professionals (Orthopedics, Neurosurgeons, Family physicians, Internists). Method The study was conducted using a cross‐sectional survey. A postal questionnaire was sent online to different medical professionals. The questionnaire included two sections. The first section was about personal demographic whereas the second section was about the CRPS symptoms, diagnosis criteria, and management. Results The study resulted in a total number of 97 participants that filled the questionnaire completely with the highest number working at tertiary health centers (59.8%) followed by primary health centers (30.9%) and secondary health centers students (9.3%). Seventeen percent of the participants were aware about the diagnostic criteria of this condition, while only 15.4% have applied these criteria before. Awareness of risk factors and complications were 22.7% and 23.7%; respectively. The mean of the cumulative score out of 11 awareness among our participants was 2.48 (range; 0‐10). 37 (38.1%) of the participant scores 0/11 while none of the participants were able to score 11/11. Our study identified an association between years of experience and the level of awareness with a P value of .007. Conclusion The awareness of CRPS among medical professionals was not sufficient to met the recommendation of CRPS criteria and guideline. Physicians from different specialties need to increase their knowledge and awareness about this conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".