Pain characteristics and imagery ability in patients with familial Mediterranean fever
Bibliographic record
Abstract
Abstract Objectives Assessing imagery ability could be important for clinicians to understand or speculate on the limit of a patient’s physical ability in chronic pain conditions. We aimed to assess imagery ability that is potentially affected in patients with Familial Mediterranean Fever (FMF). In addition, pain characteristics and associational factors between pain and imagery abilities were examined. Methods Patients with FMF group (n=30) and control group (n=30) were included into the study. Movement Imagery Questionnaire-3 (MIQ-3) was questioned in both groups to assess imagery ability. McGill Pain Questionnaire Form (MPQ) and Pain Catastrophizing Scale (PCS) were used to assess pain. Results There was a statistically significant difference in all sub-scores of the MIQ-3 imagery levels between FMF and the control group (p<0.05). Mean value for PCS was 23.27 ± 12.52 in which 13 (43.3 %) of the patients had higher scores than 30 indicating catastrophic thoughts. Conclusions It was determined that the imagery scores of the patients with FMF were lower than the control group. Patients who had catastrophic thoughts showed more scores in IVI scores indicating that the attention processes of these patients to their bodies might be affected. Further large-scale, long-term, prospective, randomized-controlled studies are needed to confirm these findings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".