Consistency and Accuracy of Multiple Pain Scales Measured in Cancer Patients From Multiple Ethnic Groups
Bibliographic record
Abstract
BACKGROUND: Standardized pain-intensity measurement across different tools would enable practitioners to have confidence in clinical decision making for pain management. OBJECTIVES: The purpose was to examine the degree of agreement among unidimensional pain scales and to determine the accuracy of the multidimensional pain scales in the diagnosis of severe pain. METHODS: A secondary analysis was performed. The sample included a convenience sample of 480 cancer patients recruited from both the Internet and community settings. Cancer pain was measured using the Verbal Descriptor Scale (VDS), the visual analog scale (VAS), the Faces Pain Scale (FPS), the McGill Pain Questionnaire-Short Form (MPQ-SF), and the Brief Pain Inventory-Short Form (BPI-SF). Data were analyzed using a multivariate analysis of variance and a receiver operating characteristic curve. RESULTS: The agreement between the VDS and VAS was 77.25%, whereas the agreement was 71.88% and 71.60% between the VDS and FPS, and VAS and FPS, respectively. The MPQ-SF and BPI-SF yielded high accuracy in the diagnosis of severe pain. Cutoff points for severe pain were more than 8 for the MPQ-SF and more than 14 for the BPI-SF, which exhibited high sensitivity and relatively low specificity. CONCLUSION: The study found substantial agreement between the unidimensional pain scales and high accuracy of the MPQ-SF and the BPI-SF in the diagnosis of severe pain. IMPLICATIONS FOR PRACTICE: Use of 1 or more pain screening tools that have validated diagnostic accuracy and consistency will help classify pain effectively and subsequently promote optimal pain control in multiethnic groups of cancer patients.
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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 | 0.001 |
| 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".