Assessment and classification of cancer pain
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pain is probably the most feared symptom in cancer, and pain control has received considerable attention. Adequate pain management requires precise and thorough assessment including universally accepted definitions; an area with a great potential for improvement. There is still little consensus on how to categorize or classify cancer pain. The recent literature was reviewed in order to evaluate the development in cancer pain classification and assessment, respectively. RECENT FINDINGS: At present, only three standardized, systematically developed but not fully validated pain classification systems exist. However, their use in clinical practice is relatively limited, with one exception; the Edmonton Classification System for Cancer Pain, which is now subject to a large, international validation study. The findings from the cancer pain assessment literature reveal a plethora of instruments indicating that tool development is a continuous process, which does not follow systematic guidelines. The driving force is most often specific research interests in a limited number of issues related to cancer pain. SUMMARY: There is still no universally accepted tool for cancer pain assessment or general agreement on which domains to include in a classification system. In order to improve cancer pain management and research, we need to agree internationally on how to classify and assess cancer pain. Consensus can only be achieved through worldwide research collaborative work employing a systematic, stepwise process based on the existing body of knowledge, patient and expert opinions and clinical validation studies.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".