Classification of pain in cancer patients – a systematic literature review
Bibliographic record
Abstract
One of the aims of the European Palliative Care Research Collaborative (EPCRC) is to achieve consensus on a classification system for cancer pain. We performed a systematic literature review to identify existing classification systems and domains/items used to classify cancer patients with pain. In a systematic search in the databases Medline and Embase, covering 1986-2006, 692 hits were obtained. 92 papers were evaluated to address pain classification. Six standardised classification systems were identified; three of them systematically developed and partially validated. Both pain characteristics and patient characteristics relevant for cancer pain classification were included in the classification systems. All but one of the standardised systems aim at predicting treatment response or adequacy of treatment. Several domains and items used to describe cancer pain but not formally described as part of a classification system were also identified and systematized. The existing approaches to pain classification in cancer patients are different, mostly not thoroughly validated, and none is widely applied. An internationally accepted classification system for cancer pain could improve research and cancer pain management. This systematic review suggests a need for developing an international consensus on how to classify pain in cancer patients.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".