The Dimensions of Pain Quality: Factor Analysis of the Pain Quality Assessment Scale
Bibliographic record
Abstract
OBJECTIVE: To provide a better empirical understanding of the dimensionality of neuropathic and non-neuropathic pain quality. METHOD: An exploratory factor analysis (FA) was performed with baseline pain quality data [assessed using the Pain Quality Assessment Scale (PQAS)] from patients with osteoarthritis of the knee (n=368) and low back pain (n=455) who had participated in a series of analgesic clinical trials. The results of the FA were then confirmed in a sample of patients with neuropathic pain secondary to carpal tunnel syndrome (n=138). Comparisons between the diagnostic groups on scale scores derived from the FA results were also made using t tests. RESULTS: Three clear pain quality factors emerged that seemed to represent (1) paroxysmal pain sensations (PQAS descriptors: shooting, sharp, electric, hot, and radiating), (2) superficial pain (itchy, cold, numb, sensitive, and tingling), and (3) deep pain (aching, heavy, dull, cramping, and throbbing). The PQAS tender pain item did not load strongly on any of the 3 factors. DISCUSSION: The findings support the hypothesis that pain qualities cluster into distinct groups. If replicated in additional samples, the pain quality domains identified may provide clinicians and researchers with a useful way to summarize data from pain quality measures, and may also provide meaningful end points that would allow for treatment differentiation between various pharmacologic entities.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".