What do tender points measure? Influence of distress on 4 measures of tenderness.
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between current pain, distress, and ascending and random measures of tenderness. METHODS: Manual tender point counts and dolorimeter measures of the pressure pain threshold were determined in a sample of 47 women representative of the general population with respect to tenderness. In addition, discrete pressure stimuli of varying intensities to the left thumb were applied in random fashion. Distress was measured with the Brief Symptom Inventory and the Beck Depression Inventory, and pain was evaluated with the Short Form McGill Pain Questionnaire. RESULTS: Only the random measure of tenderness was relatively independent of an individual's current psychological state. The respective correlation coefficients between measures of tenderness and psychological state were generally greatest for the manual tender point count and also significant for the dolorimeter measures. In contrast, all measures were highly correlated with ratings of spontaneous pain, again with the manual tender point count showing the strongest, and the random method the weakest, correlations. Linear regression analysis replicated the results of the correlational analysis. CONCLUSION: As a measure of tenderness, the number of positive tender points is clearly influenced by an individual's distress. Other more sophisticated measures of tenderness that randomly present stimuli in an unpredictable fashion appear to be relatively immune to these biasing effects, although our results obtained in a research setting have yet to be replicated in clinical practice.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".