Classification of pain in cancer patients – a systematic literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it