Pain evaluation of patients with fibromyalgia, osteoarthritis, and low back pain
Bibliographic record
Abstract
The purpose of this study was to evaluate and compare pain as reported by outpatients with fibromyalgia, osteoarthritis, and low back pain, in view of designing more adequate physical therapy treatment. PATIENTS AND METHODS: A Portuguese version of the McGill Pain Questionnaire - where subjects are asked to choose, from lists of pre-categorized words, one or none that best describes what they feel - was used to assess pain intensity and quality of 64 patients, of which 24 had fibromyalgia, 22 had osteoarthritis, and 18 had low back pain. The pre-categorized words were organized into 4 major classes -- sensory, affective, evaluative, and miscellaneous. RESULTS: Patients with fibromyalgia reported, comparatively, more intense pain through their choice of pain descriptors, both sensory and affective; they also chose a higher number of words from these classes than patients in the other groups and were the only ones to choose specific affective descriptors such as "vicious", "wretched", "exhausting", "blinding". CONCLUSION: Assuming that each disease presents unique qualities of pain experience, and that these can be pointed out by means of this questionnaire by patients' choice of specific groups of words, the findings suggest that fibromyalgia include not only a physical component, but also a psycho-emotional component, indicating that they require both emotional/affective and physical care.
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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 | 0.004 |
| 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".