Oncology Section EDGE Task Force on Cancer: A Systematic Review of Clinical Measures for Pain
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
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.
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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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