Expectation of a Decrease in Pain Affects the Prognosis of Pain in Cancer Patients: a Prospective Cohort Study of Response to Morphine
Bibliographic record
Abstract
PURPOSE: Cancer pain is a multidimensional experience that includes physiological, sensory, affective, cognitive, behavioral, and sociocultural dimensions. Few prospective studies have examined the relationship between a patient's expectation of pain improvement and the pain prognosis. The aim of this prospective study was to investigate whether patients' expectation to pain reduction was associated with pain intensity after morphine treatment in opioid treatment-naïve patients with various types of cancer. METHODS: The subjects were patients scheduled for cancer pain treatment with morphine who were taking nonsteroidal anti-inflammatory drugs daily. Morphine treatment was performed according to the standard method, including titration (NCCN Guidelines™, Adult Cancer Pain). Simple regression analysis was performed between pain intensity numerical rating scale (NRS) (day 8) as the dependent variable, expectation of pain decrease NRS (day 1), tumor types, and the following covariates as independent variables: patients' characteristics such as age, gender, PS (day 1), genotype of catechol-O-methyltransferase, total scores of Hospital Anxiety and Depression Scale (day 1), and pain intensity NRS (day 1). Multiple regression analysis was performed using forced entry methods with pain intensity NRS (day 8) as the dependent variable, and expectation of pain decrease NRS (day 1) and the covariates as independent variables that had a p value <0.05 in the simple regression models. RESULTS: A total of 100 patients with baseline data were included, and 97 patients (51% female) met the inclusion criteria. Patients with a high expectation of pain decrease NRS had a significantly lower pain intensity NRS (day 8) (p = 0.001). CONCLUSION: Non-pharmacological factors such as expectations for pain treatment could also be important factors to treat cancer pain, which might be associated with communication skills in physicians.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".