Sensory and affective dimensions of advanced cancer pain
Bibliographic record
Abstract
The present study was designed to explore the extent to which advanced cancer pain is explicable in terms of both physical pain intensity and affect. Most notably, it expanded on previous findings by more clearly elucidating the relationship between several discrete emotional states and the total experience of cancer pain. One hundred and eleven patients with cancer pain attending a Pain and Symptom Control Clinic were studied. Visual Analogue Scales (VASs) were used to quantify overall pain intensity and the accompanying affect. Then, correlations were calculated to evaluate the relationships both between and within these two variables. Overall, the participants rated both the pain intensity and the negative affect associated with that pain as high. Of the examined affective components of pain, frustration and exhaustion were found to be the most significant. In addition, some gender differences were identified in terms of frustration, anger, fear, exhaustion, helplessness, and hopelessness.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".