Oncology Section EDGE Task Force on Cancer: A Systematic Review of Clinical Measures for Pain
Bibliographic record
Abstract
Background: Pain is one of the most common complaints in individuals with cancer and can occur at any point during the course of cancer treatment. Purpose: To identify outcome measures for assessing pain and to evaluate their psychometric properties and relevance to adults with a diagnosis of cancer. Methods: Three electronic databases (CINAHL, MEDLINE, and PsycINFO) were reviewed using specific search terms to locate articles that identify outcome measures assessing pain in adults with a diagnosis of cancer. From the 1164 articles identified, 494 articles were reviewed and 22 outcome measures were selected for analysis. Each outcome measure was independently reviewed and rated by 2 reviewers using the updated Cancer EDGE Task Force Outcome Measure Rating Form. Any discrepancies between reviewers were discussed, and an overall recommendation for each measure was made using the 4-point Cancer EDGE Task Force Rating Scale. Results: On the basis of the psychometric properties, clinical utility, and relevance to adults with a diagnosis of cancer, the following 3 measures are highly recommended: McGill Pain Questionnaire–Short Form, Numeric Rating Scale, and Visual Analog Scale. Four measures are recommended: Brief Pain Inventory, Brief Pain Inventory–Short Form, McGill Pain Questionnaire, and Pain Disability Index. Eleven measures are recommended as reasonable to use, and 3 are not recommended. Conclusions: Seven of the 22 pain measures demonstrated satisfactory psychometric properties and clinical utility and are thereby recommended for clinical and research use in adults with a diagnosis of cancer.
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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 | 0.043 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".