Pain in fibromyalgia and discrimination power of the instruments: Visual Analog Scale, Dolorimetry and the McGill Pain Questionnaire.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to verify the discriminative power of the most widely used pain assessment instruments. METHODS: The sample consisted of 279 subjects divided into Fibromyalgia Group FM- 205 patients with fibromyalgia and Control Group CG-74 healthy subjects), mean age 49.29 +/- 10.76 years. Only 9 subjects were male, 6 in FM and 3 in CG. FM were outpatients from the Rheumatology Clinic of the University of São Paulo--Hospital das Clínicas (HCFMUSP); the CG included people accompanying patients and hospital staff with similar socio-demographic characteristics. Three instruments were used to assess pain: the McGill Pain Questionnaire MPQ, the Visual Analog Scale (VAS), and the Dolorimetry, to measure pain threshold on tender points (generating the TP index). In order to assess the discriminative power of the instruments the measurements obtained were submitted to descriptive analysis and inferential analysis using ROC Curve-sensibility (S), specificity (S1) and area under the curve (AUC)--and Contingence tables with Chi-square Test and odds ratio. Significance level was 0.05. RESULTS: Higher sensibility specificity and area under the curve was obtained by VAS (80% 80% and 0.864, respectively), followed by Dolorimetry (S 77% S177% and AUC 0.851), McGill Sensory (S 72% S167% and AUC 0.765) and McGill Affective (S 69% S1 67% and AUC 0.753). CONCLUSIONS: VAS presented the higher sensibility, specificity and AUC, showing the greatest discriminative power among the instruments. However, these values are considerably similar to those of Dolorimetry.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".