Bibliographic record
Abstract
PURPOSE OF REVIEW: Adequate cancer pain assessment using valid and reliable tools is essential for proper cancer pain management. Because cancer pain can be a complex construct, assessment of its many domains should be conducted using multidimensional tools. Furthermore, there is a need to develop a standard, consensus classification system for prognosis of cancer pain. RECENT FINDINGS: Unidimensional tools for assessing cancer pain are useful for measuring cancer pain intensity. Other domains and symptoms of the cancer pain experience are assessed using a variety of multidimensional tools. There is a lack of agreement on a standard assessment tool or a standard classification system for cancer pain, although research continues to be undertaken to develop such resources for clinical and research purposes. SUMMARY: Many pain and symptom assessment tools exist for use in the cancer patient, including the Brief Pain Inventory, the McGill Pain Questionnaire, the MD Anderson Symptom Inventory, and the Edmonton Symptom Assessment System, among others. Recent literature reveals the move toward translating these and other tools to electronic applications. Further study is also underway to create a standard, prognostic classification system for cancer pain.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".