Differences in Pain Location, Intensity, and Quality by Pain Pattern in Outpatients With Cancer
Bibliographic record
Abstract
BACKGROUND: Pain pattern represents how the individual's pain changes temporally with activities or other factors, but researchers have studied less the pattern of pain than its location, intensity, and quality parameters. OBJECTIVE: The aim of this study was to explore differences in pain location, intensity, and quality by pattern groups in outpatients with cancer. METHOD: We conducted a comparative, secondary data analysis of data collected from 1994 to 2007. Seven hundred sixty-two outpatients with cancer completed the 0- to 10-point Pain Intensity Number Scale and the McGill Pain Questionnaire to measure pain location, quality and pattern. From all possible combinations of the 3 types of pain patterns, we created 7 pain pattern groups. RESULTS: Pain pattern group distribution was as follows: pattern 1 (27%), 2 (24%), 3 (8%), 4 (12%), 5 (3%), 6 (18%), and 7 (8%). A significant higher proportion of patients with continuous pain pattern (patterns 1, 4, 5, and 7) reported pain location in 2 or more sites. Patients with patterns 1, 4, and 7 reported significantly higher worst pain mean scores than did patients with patterns 2, 3, and 6. Patients with pattern 7 reported significantly higher mean scores for the Pain Rating Index-sensory and total number of words selected than did patients with patterns 1, 2, 3, 4, and 6. CONCLUSIONS: Using pain pattern groups may help nurses to understand temporal changes in cancer pain and to provide more effective pain management, especially if the pain has a continuous component. IMPLICATIONS FOR PRACTICE: Nurses or clinicians who are taking care of patients with cancer should recognize that pain patterns are associated with pain location, intensity, and quality.
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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 | 0.001 | 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".