What do tender points measure? Influence of distress on 4 measures of tenderness.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it