Expert conference on cancer pain assessment and classification—the need for international consensus: working proposals on international standards
Bibliographic record
Abstract
An increasing number of cancer patients live longer, and palliative care has become an important part of their treatment. Symptoms are often inadequately assessed and managed. A significant challenge in clinical trials is to control for the variability of the samples being studied. To overcome this problem, classification systems have been developed in order to characterise and stratify patients by grouping them according to major common characteristics. The lack of agreed methods for the assessment and classification of cancer pain has been clearly indicated in clinical trials and in clinical practice and may be one possible explanation for the inadequate treatment of cancer pain. This was the background to an international expert meeting arranged in September 2009 in Milan, Italy. The primary aims were to produce recommendations on how to assess and classify cancer pain and to recommend a strategy for the further development, validation and implementation of an international cancer pain classification and assessment system. The recommendations consisted of two basic working proposals, nine specific working proposals and seven recommendations for the further development of a cancer pain classification system. Examples of specific working proposals were to include pain intensity, pain mechanism, breakthrough pain and psychological distress as the core domains in this classification of cancer pain and to measure pain intensity with a 0-10 numerical rating scale with 'no pain' and 'pain as bad as you can imagine' as anchors. The proposed name for this international standard is Cancer Pain Assessment and Classification System (CPACS).
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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 | 0.331 | 0.357 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.019 | 0.021 |
| Research integrity | 0.031 | 0.047 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".