The Multidimensional Pain Inventory profiles in patients with chronic cancer-related pain: an examination of generalizability
Bibliographic record
Abstract
This study examined the generalizability of the non-malignant pain patient profiles based on the Multidimensional Pain Inventory (MPI) to patients with cancer-related pain. Data were collected from 112 cancer patients. In total, 107/112 patients completed the MPI. Of the 96% of patients classified, only 60% were classified by the three main profiles. In this sample, there were 47.7% (n=51) Adaptive Copers, 9.3% (n=10) Dysfunctional, 2.8% (n=3) Interpersonally Distressed; 32.7% (n=35) Anomalous; 3.8% (n=4) Hybrid; and 3.8% (n=4) Unanalyzable. Because of the significantly lower pain severity, interference and affective distress scores, the Anomalous group could be considered Highly Adaptive. Given that 80% were classified as either Adaptive or Anomalous, these findings suggest that while the MPI-based profiles do apply, a two profile classification system may be more suitable for cancer patients than the usual three. In particular, the low proportion of patients classified as Interpersonally Distressed may reflect important differences in social support for cancer patients compared with non-cancer patients. Whereas the MPI-based profiles are consistent across non-malignant pain problems, it appears that the nature of cancer may affect the MPI-based profile classification system more than non-malignant pain problems do.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".