Cancer-related Pain Management
Bibliographic record
Abstract
OBJECTIVES: Cancer may be associated with many symptoms, but pain is the one most feared by patients. Pain is experienced by one-third of patients receiving treatment for cancer and about two-thirds of those with advanced cancers. To aid in providing quality care and pain relief for cancer patients, Cancer Care Ontario's Cancer-related Pain Management Guideline Panel conducted a systematic review of guidelines to provide evidence-based and consensus recommendations for the management of cancer-related pain to guide the practice of healthcare providers. METHODS: Published and unpublished cancer-related pain management guidelines were sought by conducting an Internet search, which included health organizations and the National Guidelines Clearinghouse, the Guideline International Network, and the McMillan Group. Also, MEDLINE searches were conducted for guidelines published between the years 2000 and May 2006. RESULTS: Twenty-five guidelines were found and the quality of each guideline was evaluated using the Appraisal of Guideline Research and Evaluation Instrument and the utility of the guideline for recommendations was assessed. Using these 2 criteria, 8 relevant and high-quality pain guidelines were identified. From these guidelines, the Panel articulated core principles of the management of cancer pain and selected or adapted specific recommendations through consensus to become a part of the cancer-related pain guide for practice. DISCUSSION: The domains on which recommendations were drafted include: assessment of pain; assessors of pain; time and frequency of assessment; components of pain assessment; assessment of pain in special populations; plan of care; pharmacologic intervention; nonpharmacologic intervention; documentation; education; and outcome measures of cancer-pain management.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".